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<?xml version="1.0" standalone="yes"?> <Paper uid="J79-1041"> <Title>ANALYSIS OF JAPANESL SENTENCES</Title> <Section position="1" start_page="0" end_page="1" type="metho"> <SectionTitle> ANALYSIS OF JAPANESL SENTENCES BY US I NG ~EMANTI c AND CONTEXTUAL I NFORMAT 1 ON </SectionTitle> <Paragraph position="0"> MAkBPO NAGAO AND JUN-XCHI TSUJJ I The organrantion of a nature1 langueg~ (Japanese) parser $8 d~brribed, The pslaes cw Transform fairly eoapl~s sentencrao into abtxtrau'lt s tru'rur as urked it for coae. A variation on the sys+em developed by Woads called an Aupntsd Transitio~l Netwo~k&quot; is used as tRe proaxam for anelvsis. Tho parser utiliscb detailed semantic dictionary descsipedcans and contextual infamuat ion ~hst rac ted from sentences aqalvzad in sequence. It is clninncd that ineuirivc reRtsonlnp, which is nut easilu formdieed hv rigid lagiaal aperatiena, @lass an fmpartent role in language understanding Soor intuit i\&quot;elv appealin8 scheous cf representation for both the semantic descrihians of wolds and context are discussed tkanlngs ~f verbs are described by using a case' concept, Additional ~nkormstisn is attached ta case frames of each verb te, indicate what changes the \case elements in the frahve my undergo and w'lat events mav occur in suqcession, bnings of nouns are also exqnessed in cdse-frame-like dcssriptions. Notins alscl kgve relational slots which must be filled IR b other GJO~~S of phrases, The context Is repregented in a form similar to that uf tke semantic n~twork of S-tm~ns sr the nodespare of Norwn along with saw dded special. 1ic;rs (KS -</Paragraph> <Paragraph position="2"> contain objects mentioned ir prevlous sentences or pendlng problems whlch ma be resolved by succeeding sentences The objects bin WS are ordered according to their degrees f importance In the contest. Several new techniques based on heuristically admlsslble operatlone are presented to analyze 1) comple and long noun nhrases 2) conju~rtive phrases 3) anaphosic expresslan and 4) omitted words in phrases or sentences The resulrs ~f applying the parsing program to tNe sentences In a textbook of elementarv chemistry are also presented,</Paragraph> </Section> <Section position="2" start_page="1" end_page="1" type="metho"> <SectionTitle> ANALYSIS OF JAPMESE SENTENTES BY USING SE </SectionTitle> <Paragraph position="0"/> <Section position="1" start_page="1" end_page="1" type="sub_section"> <SectionTitle> 3.1 Properties of a Noun Phrase '2 2 3.2 Analysis sf a Nom Phrase 2 7 3.3. &alysf% QE Conjunctiva phrases 32 3.4 Analysis of a S~mple Sentence 38 </SectionTitle> <Paragraph position="0"/> </Section> </Section> <Section position="3" start_page="1" end_page="1" type="metho"> <SectionTitle> IV CONTEXTUAL ANALYSIS . ........t . . t . 46 </SectionTitle> <Paragraph position="0"/> </Section> <Section position="4" start_page="1" end_page="1" type="metho"> <SectionTitle> td MALYSLS OF GQWLEX SENTENCES . I .*+. 65 VL CONCLUSZQP . . . * ...... ... ... . . .71 I INTRODUCTION </SectionTitle> <Paragraph position="0"> Ia this paper we describe the ssgsnitation of the natural language passes dewloped over the last twa years. T11ia fotms an important paet uf s question-answering system under de~@logmnt with natural language (Japanese) input. T11c patsar can transform frnir-tv cumplczl, srrltepces into abstract structures marked for case, It utilikes detailed srpbmntic dictionary descriptions and cont~xtual infurmatton skutract~d from th~ preceding sentences.</Paragraph> <Paragraph position="1"> For: the present, we heve confined the dewin of the system to the field of elamentary ehemlistrj where we can describe the semantic world in rather concrete terms At the same ti=, various compleu- events occur ln this field For example, substances which partleipate in part~cular events may disappear, new substances may emerge, or some properties of the substance? may be altered To treak these comple situations, it was necessarv to formally represent relationships between events and cl~aages of state and to devise an appropriate schema of representing context, In mst appr~ache~ to the understanding of natural barlgunpe tl~roupk artificial intelligence, schemas which entail rigid logical operations are used to represent both knowledge and context Loglcal operatlons appear to be necessary for solving some kinds of problems in natural language, especially at the deep deductive level of unders tandlng. However, lntui tlwe reasoning is not easily formalized in terms of loglcal operatlons It is our contention that intuitive reasoning 1s completely based on the language activity in the human brain Associative functions relating to semantic similarities between words, semantic depth of an interpretation and probability of associative occurrence of events are inherent factors in intuitive understandinn and the seasoning process, Y. WII~ES (1975) in his eykatem carries out intuitive r~tarsoning by employing the notion of 'eemntie preference'. His system seems to work well an anralytfng local relationeahips among words, Mowever, in order to analyze amre global relationohips (e.lg., in dealing with complex cams of azlaphora) we require access to we information than can be contained in formulae (templates) a ed with the haxicon. We find Wilks' uss of '6s-inference rules' sahkar awkward. The system w~uld be much improved if accoarpanied by afi appropriate achema for representing context.</Paragraph> <Paragraph position="2"> v Case gsam~r' ~entence-analysis theories such as those of Fillmore (1968) and Celce-Muscia (1972) are based on the semantic relat ibnskips between verbs ad nouns -- events and concepts, R. F. Simmaas (1973; 19751, n (3973), D, E. Rumelhart (1973) and so on follow these theories to represent knowledge and context in their systems. We also adopted case s and nsodifded it to account for Japanese sentences. We represent cantext fn the form of a @@antic network. An input sentence is transformed into a c~rxeeponding deep saee etswcture, This structure is assillllsilated with the ~e tie network constructed from previous sentences, Japanese is a typical SQV language. The word order 1s rather srLItrary except that the main verb comes lest. Cases such as subjective, ~bjective and dative as syntactically indicated by postpos~tions, but a petposition can be used for several deep cases anibiguously Hence the determination of underlying senteatial structures rests heavily on an undarstarading of the semantic relatione between the main verb and nouns kloreover in Japanese the words which are essential in understanding a sentence are often omitted without pronqminal reflexes.</Paragraph> <Paragraph position="3"> Our system can - 5 infer from the semantic ddscriptions of wards what kinds of ghzsses should be supplied to fill lexieal gaps ?nd sgarch the contextual tcpreaentation to find appropriate fillers.</Paragraph> <Paragraph position="4"> The final analysis produced by our pamor ia a srtwntic netwvtk, This could he used fox the internal ,raps@sentatisn of data in a qupgtt~ftanswering system ox as an interniwdiete e~pr~ssiun in '61:mchin~ txansl,stiurt H~wu\!r, it is still too early to report on th~. rcsults of these ~ISL~~U~C~CIJI applications.</Paragraph> <Paragraph position="5"> The paxscr cottsists essrntisll wf four fixed cornp~>pcArs* I$ The grammar consists of rules written in PLATDE;, PLATOK 1s a new programming language which is a variant of the sqqtem deuelwped by Woods (1970) called 'Augmented Tkansitlon Network PLATON has add~tlonal facilities for pat tern matching and flexible backtr ackinp, A grammatical rule in PIATOW consists of two parts pattern rewrite which 1s expressed as a pair of syntactic patterns, and semantic and contextual check bhich is an arbitrary LISP function. Ffllen a rule is to be applied, the semantic and contehtual chech is emplnved to determine whether the rule is mwnr ically and csnteutuallv feasihlo, For the present we have about two hundred rules for the analysis of Japanese sentences, These rules are devised to combine Various syntactic patterns in Japanese with appropriate sewntlc and contevt ual checklng functlohs, PLATON is presented in more detall In another papec by Nagao and Tsujil (1976).</Paragraph> <Paragraph position="6"> 2) In the dictionary are stored words along wlth their various semantlc relatlonshlps. We express the meaning of a word in terms of hov it may be related to other words, The meapinq of a verb 1s described In the form of activity patterns' in the verb dictlonarv An actlt~itv pattern -6is actually the case frame of a verb and additional related information. The case fr regresents what case relations the activity entails and what kind sf referents will be appropriate for each case slot, Adartions1 information provided feeds into the 'change' or 'causative' compahent used by Norman (1973).</Paragraph> <Paragraph position="7"> Such infortnstfdn indicates how one activity pattern my be related TO another by causal re, onghipe and what related change may occur in the eeatlantie network representing cbntext, From auch inflarmtion we can Infer what activitiefi and changt! will. foll~-)w the pre~enfr ~ctlvity, The meanings of nouns are also expressed in the caee-frame-like description8 They also haurn relational slots which will be filled in by sther words or phrases, 3) The ~onterrtual representation is similar in fom to the semantic netw~sk of R. F, Simmons (1973;1975) or the nodcspase sf B, A, Nomn (1973). In tk~s representation there are two kinds of nodes. The C-node esrsespands to a concept typically expressed by a noun The S-node correqponds to an event. h event is .rs realization of an actian pattern and each argument of the pattern is assigned a C-node. C-nsdea are related to S-nodes by the case labled rclationa. These ~latbons are bidirectional The fallawing kist shaws the relations used in the network: (1 Deep Cue Relations. ACT', OBJ, PUCE, TIHE -- A deep case relation connects an S-node with its argument C-node, (ii) Attributive Relations: VOLUME, (r;OLOR, MASS, SHAPE -- An attribute relation connects a C-node with its value. We Gan dlstingulsh two C-nodes associated with the same lertical entry but different values of attributes.</Paragraph> <Paragraph position="8"> (iii) Taken substitution. TBK -- TBK is used to connect a node with -7a lexical entry, (iv) Went-krent Relation MUSE, IFPLY -- Two S-nodes arc somtiws connected by n particular relation. The rclntions arc roaatims cxpscqst*d explicitly in the ~urfsjce sentenre hv a spocfal romjtmrtl~n sue11 aa</Paragraph> <Paragraph position="10"> In Our system the somantic network is acco~anicd with spt.cia1 Lists (Noun Stack-NS, Hvpathatirel Noun St,~ck-HSS, Trdpping L~z-TLL Ve call thqsc li~ta intrmrdiate Term bbmxy. Contcxtnal fu~~ctiuns votk on these Sis ts to seaxck appropriate nudes of the semantic ne twark correseend to the referents sf snaphosic expressions or the unex~sessed rLewnts of sentences, (4) Selaant~c and Contextual functions are propmmned in LISP These functions are lncorposated in the PLATON rules along with rewrltlng patterns A contextual function takes as arguments the seaantls constsalnts a target node must satisfy and returns the node when an appropriate node rs found from the semantic network A senwntic fu~ction cheeks dcscriptiuns in tt~c dic ~~QII,ILY te7 dc turnline ~~hcrt~er tEw cwrnhin,~t ion of two 'r~ards is sen~anticallv pedwsibl~, For analp zing nourl-nc7tnl combir~ations , wc provide sixteen semantxc functions.</Paragraph> <Paragraph position="11"> The whole system is wrltten in LISPl.6 whlch 1s implemented on a minicomputer, TOSBAC-GO. The mlnlcomputer 1s equlpped wlth 64KB as main memory and 512KB as secondary core memory The LISP1 6 uses the secondarv memory as virtual storage The drctlonary conslsts of about 400 nouns, 200 verbs and lLQO adjectives and other categosles The parser with rules and the dictionary occupies about 50K cells The LISP uses a soft-ware -8paging mechanism, and the main memory is rather smell in comparison with the secondary mmory The content of the dictionary is stored on a disc in the form of S-expressions.</Paragraph> <Paragraph position="12"> Consequently, the speed of execution is slow.</Paragraph> <Paragraph position="13"> It takes typically about 10 to 15 minutes to analyze sentences which oontain</Paragraph> </Section> <Section position="5" start_page="1" end_page="2" type="metho"> <SectionTitle> 3 to 5 simple sentences and 10 to 15 noun phrases. XI LEXIUL DFSCRlPTIONS OF WOW 2,-1 Now kscripkiern </SectionTitle> <Paragraph position="0"> &st nouns have a definite meaning by themselves. We call these Entity Wows. kn entity noun is considered to represent a set of objects, and therefore is taken aa a name of the set. The sbjects belonging tta the set $hare the same properties By lntsoducing another property the set my be divided Into a number of subsets, each of which is expressed by another noun, We describe such set-inclusion relationshrps and set propestles ln the noun dletfonaq.</Paragraph> <Paragraph position="1"> We represent a property of a noun by an attrxb-ute-value pair expres~ed aa (A Vb . For instance, the dirtinner entries for the nouns 'wtcsiel' and ' liquid' are :</Paragraph> <Paragraph position="3"> Lack values (V) showing chat material' may have arbitrary values of these zttributes, In the definrtlon of 'Ilquid', there 1s a $P-link to 'raaterlal', which meam that 'material' 1s a super-set concept of 'Ilquld', or that s subset are considered to have the earn properties 18 the abjeca laf tha super-oat, in addition to the proprties described axglLcitLy in its dafinition.</Paragraph> <Paragraph position="4"> By the above daecrhpticor, rn ran sea that %k@ value bf the at tr%bure STATE af 'liquid' Ler *LIqWSB, and that u8f %WE LB th~ I~QI%L~$ V~~LUQ 85L *SYtla *LLQWXD he ona of rha pridtiws vrllua mrkrrs, Thr prleieim voZur mrkers are indicatad thl~ the preceding J&quot;na value NIL indfcstab that 'liquid' can not have any value of SHWE iv trrcin~ up thc SF-links, uc ran rstrlew all. the (A V) pairs of an o'oj@wt. We assum thc value of an attribute of a lewas coneept has precedence over that of eha upper concept Far inutmca we csrl obtain the following full, description of ' liquid' liquid ( (ATTR (STATE ELIQUID) (WE NIL) (INSS) (COLOR) ------ 11 These upperlower relatianships smng entity nouns cr$ not expressed by a tree structure SQ~ nsuns may share proparties with more than one noun 'Water' is such an example -nter1 has the properties OF both 'liquid' and Although ast nouns are regarded ae entity nouns, there are a few nomg which hawe relational function@. Mte call them Relational Noma; 'Fathart ka r famili&r a pie* In order to identify a pesaon indicated by thc word, mi hrvt EO haw whose father he ie.</Paragraph> <Paragraph position="5"> In the chemical field we ran auily find auch nome (e. 8. , 'weight' , ' temperature' , ' color' , qnnd 'mess ' ) *E&quot;hrdss &re erJlcd Attribute Wslane. Their maninge are deacrilaed in a different way from that of ordinary nowe, Figure 2.2 shews some @,xempliea. tiare, A-ST draipater, the s tmdsrd attributive rcldtion which is expressed by the word. The description (t4F N-A) shows the noun belongs to the group 0f attrlbuto An attribute nsun may express amre than one standard strtibute. WKISh (size) expresees VQL MASS, LEHG;TM QR AREA. The attsibutc it expreoqes in csntext depends upsn what entity noun is used dth it.</Paragraph> <Paragraph position="6"> aft Attribute noun8 am further ~Zaseif ied inta twa groups, quantitative and qualitative A guc*aitati~g attribute noun cannot be a case element af a verb wtaech regwfres guntitntive nouns. Tna verbs FUERU [increase) and MERU (deciease) are such &xamples gf verbs.</Paragraph> <Paragraph position="7"> Figure 2 2 Attribute Nouns 'Liquid' is another relational noun. The Japanese word whirh csrreapoads to 'liqula is EKITAI. While 'liquid' in English can be elther a now or an adjective, ERITWI in Japanese is ca~rgorrzed synta~t~calJy as a nom. But @emantically EKLTAF has two diffe~ent meanings, one corresponding to the liom usage of 'liquid', the other corresponding to the adjective usage of it. The noun EKITAI in the adjective uasp is collsd a Value Noun wf th the! attribute STATE. Anofekes word IRB (scd color) 18 alga a vrlua noun of the attsibuta COLOR. Figure 2.3 ahws rlrc da~cription of th~n~ nouns in the noun distionerv,</Paragraph> <Paragraph position="9"> There are other kinds of relational nouns Action, Prepositional, hnphorie, and Function nouns, h action noun 1s the nsmnalization of ,I verb. For example, KANSATSU (obserwat~on) is the nodnallrzation wf the ~erb Q'iNSATSU-SlrRU (~bscmel 'bu'c describe this in the dicti~nax~ kv giving a link to the ~xigit~~3.l verb and bv adding other infua;mtS~?n There arc nut t positions1 pb%rtic9cs Ln Japanese fur r.wr\ preposition in English, Sow special nouns plg the sole af English preposltlons. We call such nouns Preposltlanal Nouns Because a prepasltl-onal noun usually has more than one meaning just as an Engl~sh preposltlon has, we attach semantic conditions to help disambiguate then Flgure</Paragraph> <Section position="1" start_page="1" end_page="1" type="sub_section"> <SectionTitle> 2.4 shows example of leslcal descriptions of prepositional nouns </SectionTitle> <Paragraph position="0"> Corresponding to each waning we give a triplet The Elrst element is the aemntic condition, If the ~ondition is sutlsfled, the corresponding second elewnt 28 adopeed as the waning, If not, the next triplet is tried The q~sond clcmnr of B triplet represents thc wt~ole waning of the phrase. For exrqle, rb *ole maning of the phrase TStKrtiUE (desk-entit ncun] NO [of1 Ur: [an-prepositional noun] (on the desk) 1s PLACE The third element of a rsiplae expregnoe she reletionstlip hk which the other notln ~n the phrase wy ~w~iajibze eke whole maning.</Paragraph> </Section> <Section position="2" start_page="1" end_page="1" type="sub_section"> <SectionTitle> 2,2 Verb kacriptiora </SectionTitle> <Paragraph position="0"> Verbs, adjectives, and prepos~tions in Engllsh have relational -wing@ wlth nouns. A verb represents a certaln acti~ity, Fih~le the agent mooc~eted wzth the activity is not ~nherent to the meaning of the verb (neither is the object the activity affects, nor the other components) These components appear in a sentence with certarn loose selatlons to a verb - 13 In our system the meaning QE a verb is d~~csibed by settrirzg up k;emsal relational slots whLch will be fillad in by nouns, JR this sen&@ the waling of e wcrb is not confined to itself, but tw relrtgd to mma.</Paragraph> <Paragraph position="1"> We describa rhase relations by u~ing the cone caneept intr~dured by 6. J. FIllwre (1968) Case GZBY be Iiaohed UPCI~ 18 a mla which an rbjrct pknys in sn actlvitv, Because scwllraJ. abject& ~,uuallu pertizipare in an activity, them are s~varal easeas aasncistrigd wit11 an aitrfvit%. kl nbfcget ie e&$raascd b) a twun phrage, and wn activity hv a wrh, A sentence instantiates afi activity b) supplving noun phrases to the cases associated with the activity Me call such instantiated activity LEC~ Et*~ntr The prslskem is tea decide what case a noun phrase holds in selatisn ts a verb in an\ particular event.</Paragraph> <Paragraph position="2"> Tbu& there are usually some syntactic chess in a sentence as to how it instantiates an uctfv~ty, they are not enough to decide the ease rekarionships between nQu;n phrases and a verb. To establish these relatiomskips we need both svntactic and sc~k~ntic information, A verb has its own sp,t.cinl. usage patterng, Z&quot;t~ct is, certain caws ate ngcewsaxy for the activity and certain objects nx+e prclernhla 8s fillare fur the case. Q@ aall these labled patterns Case Frames for Verbs, and express them ss a list of case palrs such as (WE NOW) A verb usually has are than one case frame corresponding to different usages, A typical description of a verb is shown In Figure 2.5 According to this description, we underskand the verb TOKASU (melt, diassolve) hae two different usages. In one usage the verb takas thc ACTOR case, and prefers to take the sub-concepts of the noun NINGEN (human being taa the case element, In suck a way case f ramc descrip tians are closely tied to noun descriptions, espec~a1l.y with the upper-lower concept relationships amng nouns.</Paragraph> <Paragraph position="3"> Rere are twca types of cases, Intrinsic and t xtrfnsic cases, The iratrinsis caws st a verb ate eesantial ones for the ~ctivhtv, but extril.lsie: caw@ am not. For exampl~, the cases of TIME and PUCE, which express when and where an event occnrs, are extrinsic f~r ordinary verbs, kWst aetbvitxes can be modxfied by these extrlnslc cases, bur the kinds of nouns preferred for these case elements do not s tsongly depend on the kinds of activities* Therefore we descrrbe only the lntsinslc cases In the verb dicrj.onaky. We set up fourteen cases as shown in Table 2.1 for the analysts of sentences In a textbook of elementary che&str]i</Paragraph> <Paragraph position="5"> He putq sulfur in a test tube In the chemical field, o chemical object is aFtrn scpitdtd ss ACTnr of ,an action, thouah it dors not ~xcrcise i$ten,eiun in lrpetd to actionmJ ror OX~IP~~ the unde~lincd ward in the follc?w?kR erntTncr is rr~ardad aa ACT. (3) OBJ OOBJect is the ~ecci d11g end of an octiwitv. It s affected by the activity, (a) IQiRE-GA MIZU -0 - NESSLhRU he-fS1M) water - (OBJ) - heat He heats the water.</Paragraph> <Paragraph position="6"> I__(b) TNSAN - GA AEN -0 - - TOUSU ydrochloric acid -(ACT) zinc -(OBJ) -- me1 t Hydrochloric acid pelts zi nc .</Paragraph> <Paragraph position="7"> (4) IOBJ This case 1s semantically the mast neutral case, It is an object or concept which is affecte? by sn actlvlty, and which is not OBJect, This case is usuallv specialized by the other cases such as PUCE-, TO, IN and so on, depending an the semantic interpretation of the verb ltself In .order to rq~olve the cayntactic ambiguity of a sentence, it is also nscaoaery ts utilize Contextual Snformtion obtained from preceding sentences. hen one knowa a certain event has occurred, he can anticipate succe~sive events that will occur and what changee the objects participating in the evant will undergo.</Paragraph> <Paragraph position="8"> Thie kind of expectation plays an important role in underetanding eentences. Various kinds of associations clucter conceptually arsund inddvfdumb activities, One can perform eontcxtunl analysis of language by explicating these associations, We append this kind of experiential knowledge t/b the case frau~s of vetbe The follawing two items are described for each vcib in the verb dictionary: (1) CON thie refers to the conseqdent activities which are likely to Eollow the activity sf the verb, but not necessarily, (2) S this refers to the resultant effects on objects in view of how the objects are influenced bv the activity. In our system the influenre on the objects is described by the following three expressions: (a) ( ADD case a-set-of-(A V)-pairs ) (b) ( DELETE case a-set-of-attributes ) (c) ( CREATE lexical-nam-of-an-objec t a-se t-of- (A Vj -pairs) (a) mans that the object rn the case indicated by the second element comes to have a set of prgpertles lndrcated by the thasd element. (b) is for the deletion of a set of properties from the object. (r) shows that some objects will be created by the activity.</Paragraph> <Paragraph position="9"> In this expression one can see the verb TOKASU has two differetlt meanings. One cottesponds to 'melt', and the ather to 'dissolve in'. When we analyze the aentense,</Paragraph> <Paragraph position="11"> WP adwt the firgt enae fraw of TOKhSU (melt1 because it gives the highest mtched value against the sentence (aee section 3.4). As the result ~f evaluating the MWS exgrefssion in the case frame, we conclude the copper is nm in the liquid state. In the lexical description copper' is a lower concept af 'solid', so that copper in general behaves as e solid object. But the copper in the above sentence comes to have the attribute value pair (STATE *LIQUID) and will behave as 'liquid' in the succeeding sentences. On the contrary, when we analyze the sentence SNIQ -0 MZZU -MI TOUSU aalt -(OBJ) water -(IN, PLACE, etc,) melt, dissolw Soeane diswalves salt in water.</Paragraph> <Paragraph position="12"> the second case frame of TOKASU (dissolve in) gives the highest matched value After the sentence instantlate9 the case frame, a new object (l,e*, a solution which conslsts of salt and water) wlll be created.</Paragraph> <Paragraph position="13"> CON AND iYl'MTS are thus important in the contextual analysls of sentences. The detailed analysis procedure using these expressions iS described In sectron 4.2.</Paragraph> <Paragraph position="14"> rIr MALYsrs OF NOUN PHRASE</Paragraph> </Section> <Section position="3" start_page="1" end_page="2" type="sub_section"> <SectionTitle> 3.1 Properties of a Noun Phrase </SectionTitle> <Paragraph position="0"> In Japanese, two or mote nouns are often conentcnnted by rhc pastpaeftion NO ta form a noun phress, Because there are any dhff~tent sewantie relationships smng nauna concaten&evd by NO, we nust Secfdr kkat relationshipe may hold among the nualns. Qpical caamplcs arc shoty% in Figure 3.1, EhitfTAJ: JOLTAS. -NO SMSO -8 ThZSEkl aiquid a tate Q= gem VC.~UW the volu.wac) of the oxygen in the st&@ of liquid M@U -NO AT -NO ~vATORIL?!IJ -NO TAISEkl -NO IJE2K.A react ion after sodium 1 UIB~ change changes of the sodium's vulume aftel the reaction Eigurc 3,l. Examples of NOLWNO phrases Thc phrase NOLWNO can mdift , in principle, anJ or ,111, of the suc~e~dbny modification relationships are syntarticallv permitted, frde must decide which one is correct by considering semantic restrictions.</Paragraph> <Paragraph position="1"> We have identified slxteen semantically acceptable NOUN NO NOW combinations. These are shown in Table 3.1. (See pgs. 23-25 for th~s table ) Corresponding to these relationships we prepared sixteen pslmitlve functions. These functions are applied in turn to a noun phrase to declde what relat~onship holds between two nouns, The order in which these functions are applied 1s based on the frequencj and the tightness of the relations, Each function checks only one semantic relation Xn order to .illustrate how these functions perform their tasks, the following exaxiple of noun + prepositional noun' Japanese, so= nouns are used te elucld,3tc the cb3sc relationships hr tween s nQwn phrase and 3 verb TI72 noun &quot;SIE ;tn this c\ -qplr.</Paragraph> <Paragraph position="2"> expresses ck;lslo.; such 3% CAL'SE lsr P11IPOSE.</Paragraph> <Paragraph position="3"> The Elrst entltj noun 1s a constltuent element of the object uypressed 'try the second noun (12) ( entlty nounlS(entit\ noun) (ex) SANWIOU -NO SAlYSO* oxidized copper oxygen *The second noun rs a constltuent element of the object expressed bv the Elrst noun TABLE 3.1 continued (13) ( entity now)+( entity noun) (ex) SH'3BKmM -E30 SOKO* tcet tube bettsm *The second now refers to part af the object expressed by the first noun.</Paragraph> <Paragraph position="4"> (14) ( entity nsaasrs)f( entity noun) (ex) URI HATWRI -NAM -NO KINZQKU* pstasalua, sodium ~tc*. metal 1 *The nouns 'potassium' and s~dium' are lower concept nouns of the last noun 'mtaX' (15) ( na=)+( noun) (ex) SHITSmYQUHBZOW -NO NOWSQKU the conservation of mass law (ex) lcrn ATMI -NO CW1UR.A per lcm2 pressure phrase is ginn.</Paragraph> <Paragraph position="5"> The noun IYUE is a prepositional 11c7wB and its Rrwuslltic d~q~riptlon is shown in Figure 2,4. Wc. note thst this world has two JL t fetk~nt owanitrtgt*</Paragraph> <Paragraph position="7"> The fut~cttun far tklr m;;ldlpYis of this kit~Q of ~II~QSC c,.hec,'k% ,zt titst this function fails and laturn$ the value N3L. In this cndnplc, b~ca~~sv the word bL'2.E is a prepwsitiunczl noun, the checking proceeds furtIlt91 Tl2e description in Figtrle 2 4 shws that if the preceding noun 1s an action noun (i e, , if it is a nodnallzatlon of a ~erb) then IkE has the f~rst maning. Recause the noun JIWX (e vperiment) tjatisf ics th~s condition, the checking succeeds and the function returns the value T The result of the annlvsis is skoim in Figure 3.2 (a\ , thc other hand, iE the input is TStKlT -Nc3 lL\E desk before, in front of then the word TSUhUE (desk) satisf~es the condithon of the second meaning, and the result is as shorn In F:igure 3 2(b$ , Figure 3 2. Results of analy~es of noun and prepssftdsnal noun phrase In thls ray the sixteen checking functions not onl? test whether a certain sewntic selat~onshfp h~lds awng input w~rds, but also disambiguatts ehg meaning4 of input wsrds Wc analsrc? a maan phraec bv using the above sixteen rheck~ng function@ eubject to the linltation that selzied noun gceups aav nc?t overlap stated before, noun + pas tposltion NO' phrases and ad.iectives can modify only the eucceeding nouns. We stac~ in the temporary aack noun phrases and adjectivee for which the nouns to be modified have not been deteruned, The analysis of a aorta phrase IS carr;ied out by scanning words one-by-one from left to r:ght, If we scan an adjective or a dererrmner, we stack the# word in the temporary stack. If we scan a noun, we p~ck up a word from the tempciar stack and check whether: it can modify the noun. mi8 checking 1s done by the above f~rfons if the stack word is a now. Me 8190 have the checking functions mlating nouns to adjectives ot detedners &quot;a&quot;r;le dictimary, cantent: tsE arr adjkzrtiwe is brnt the sew a@ that ut a valt~e The gewntic checking funrtlvn hetwccn &III adjective and w nebv vhbl teat whetbar the noun can have the attribute which is mdftfkakl~ by the adjetti=. The chocking of the determiner differs ~naarcawhnt and is expleincd in a Isrer chaptor Thv o~PIEIIII~ ~KQCCPI wikl stop wllrtl there are nv words In the temporary stack or a wpJ is picked up that fails to w$ify the noun bring gcanncd, The now1 i$ then stacked in the temputav stack, If the ee stack contarns only one noun and there are no words EUa be scanned in the noun phrase, the analksis succeeds ad returns the noun In the stack me returned noun is called the Mead Noun of the noun pt~sase These processes are ~llustrated in Figure 3,3. (See pgs 3-30 fur this figure If th~rc are no words to be scar~ned next and the t~mp~)r~r;~&quot;fy st~ck contains than CYIC word, the11 tk~ anal\$ib fails and backtracks to the dcrisiun patr~t~ of thc program, h d~?ci?*lo'tl puint in thc ~xrtalvsis of a ni phrase is snv point at which two words haw brezr~ rolated semntLcral1.p. relationship between two words established during the analvsis is the? me determined by the function which succeeds first. Because the order of checking functions is somewhat arbitrary, in some cases a relatxmship Wcfi has not been checked may be preferable to the established relatiooship. lhis is illustrated in the examples below SHIKENW -NO NAKA -NO AKAIRO -NO EKITAI test tube in red liquid but .it failed to establish o new concept. Therefore, 'red' is placed on the top of TS* test tube -NO in -NO r~d -WB scanned word *The next scanned word IS 'liquid', Since it is a nourf, a check of the relationship between the noun and the wsods in TS is perforcxed. The check succeed8 because the combinations (value noun)+(en tity noun) and (PLACE)+ (entity noun] are semantically permissible.</Paragraph> <Paragraph position="8"> test tube -NO in -NO red -NO llquid Figure 3 3 fiere are no words to be scanne'd, and the TS contains snlj* one word, Hence, the analysis of this noun phrase succeeds.</Paragraph> <Paragraph position="9"> Ihe result is as follows. (The hccld noun of thi.; noun phrea~ is '1 Lquid' * \ sxysen in the liquid ~tate Jn the ldr~t examle the ward Jf)UTAT (state) designates an atttibute of EKITAI (liquid) end EKITAI co~responds ta a visible, teal object . JDUTAl (ststel in the second example dig'lgnatea en attribute of SANS0 (oxygen), and the w~rd EKITAl doee not correspond to a real object but is used to specify the attribute 'state' of the oxygen. hese examples show that the word EKZTAJ (liquid) has two different usages. According, to these usages, there are two dffbereqt eee~antic construeticans sf the phrase EKXTAI-NO JOWAI as shm in Figure 3.4.</Paragraph> <Paragraph position="11"> an abject: Figure 3.4. Two different deep structures for the phrase EKITAI NO JOUTAf Because we analyze a noun phrase from left to right, we cannot determine +ich usage is correct unti,l we recognize the rightmost word HENKA (change, transltioo) or SANS0 (oxygen) . Hme~#er, a semantic checking function disadiguates the multiple meanings of the word EKITAI. If the disambiguation 14 recognized to be .hcorrept in subsequent processing, we must be able to backtrack to the decision point at which this temporary disambiguation was made. We implemented such a process by using PIATON'S backtracking facilities. This pxocess is illustrated in Figure 7.5. (St*c pps. 33-34 for this figure),</Paragraph> </Section> <Section position="4" start_page="2" end_page="2" type="sub_section"> <SectionTitle> 3.3 Analvsis of Conjunctive Phsrnse~ </SectionTitle> <Paragraph position="0"> The words in Japanese which corraspor~d tu and' and 'or' arc and (closed lis tine) and (open listing) In Japancsc as wcll AS in English it is difficult to dt.tr.rtu;lnc. tilt. scope of a co~ijut~ctio~~, R~PP are som ~htdses whit11 h6~we the saw sbntdctic structure, hut semnticallv Jiffctcnt ronstxurtaons. Sonw cyamples drc shotm in Figure 3.6, On the other hand, scsw phrases have different surface structures but convey the sane meanlng as 1s Illustrated Ln Figure 3 7 As there are few syntactic clues in these examples, we must analyze them b) uskg semant:ic information. (See pgs 35 and 36, respectively, for these f:igures) At the first stage of the analysis of a noun phrase, we try to find conjunctive psstposit:ions If we cannot find them, the normal analis is sequence described above is applied on the noun phrase, If there is 3 oonjunctiue postposition, the following steps are performd %t this point, the Elrst meaning of 'liquid' has been adopted because the checking fu;nctlon for (sntlty noun)+(attrlbute noun) 1s applied before the fwcti~n for (value nom)+(attribute noun), That is, the word 'liquid' indicates a physical object.</Paragraph> <Paragraph position="1"> **The semantic check between 'state' and 'oxygen' falls, because the attribute noun 'state' has been linked to the llquld by the relation UTR-ATR and an abtribute noun cannot be linked wlth two different entity nouns ***So the program will go back to step (11) .</Paragraph> <Paragraph position="2"> *The se~umtic chcck b~twecn ' liquid' ;and 'a~etr' procr.cds tin thet * Thc semantic chrehing function fo~ (vsluc naun)+(attiihutc noun) suc~ct. Jr;. This function adopts thr src0tld mnninp, of ' Ilquid' .</Paragraph> <Paragraph position="3"> *At this timc, because the noun 'state' is onl\ linked to the lalur *LIQUID. the check between 'state' nnd on*genV succccds The result is as f follows, Notice that the WUI~ liquid' does not rwress a real object but the value of the attribute 'state' another pos tposition TO in the succeeding part (Figure 3.7) . Hence if we find M in the phrese, we do the following; if not, go to step 2. We aearch for the wecond postpasition in the Bucceeding past. If it is found, then the noun phrase before the firet postposition and the noun phra~e interposed between the fi rvt and the second pog tpssitiona me paralleled. WQ eaoploy the norm1 noun phrese anaiysia to the interposed noun phyarae, ther Rn to etey 4, TE w cannut find the wecod pnstpoeitkun, we then 80 to st.ep 2 2. If a sonjwncthw postposition i~ not TO, or there is no eecend Ta, we execute the follawing sbbsteps. (Noun-l de~ignates the noun befom the firax postposition .) a. Searefi far a noun den tical to Noun-J- ir the suceexding part. If Eomd, let it be Noun-2, and ga to step If b. Xf Nowt-h rs not an entity noun, then search for a noun which belongs to the same categorv as Noun-1. If found, let it be Noun-2, and go to step 3 c?, Search far a noun which hiis an upper concept in swmn with Naun-h. If found, let it be Noun-2, and go to step 3.</Paragraph> <Paragraph position="4"> St9 1. The phrase between the postposition and Noun-: are analyzed - - -1 noun phra~e analysis. This is now the second of the two pasallel phrases under csnshdesa tron, Step - 4. The phrase betore the postposition is analyzed by the normal nsm phrase analyeis Seep - 5. It is necessary to deterr ine what portion of the phrase befare the postposition relates exclusively to Noun-l To determine the left end of the Noun-1 phrase (e.g., in Flgure 3.8 below), we pick words one-by-one - 37 from left to rtghk, and check whether each word can modify Noun-2. The first word found which eennot modify Wsm-2 is considered the left and of the first phrase (Noun-I. phrase).</Paragraph> <Paragraph position="5"> two different determinations uf the left end eP the conjoined phrase 6. Words to the right af Soun-2 are checked to deteim&nr their - null relation to the conjunctive firdse and its tonjunsts Checking pr~cceds from left to sight The. an~ltlsis of the fo1loi~';lng ph11~Le 1s Y11u~ltr;lt~d ~n Figtl~e 3,Q * RY lTEi4R7l1 -Ilu'O RJL7 -TL~ Fill' -YCl 1 -7 HI copper sulf id63 coppc~. (and) aultul EV~\; ,S tat ao the ratio hctween the smss of the cllrpcr .~nd thr sulfur ihich con.;titutc soppel sulf idc (See pgs. 39-40 for this figure ) cbnjmctive phrase? are checked agalnst nouns in the ? portion following Sam-2 Because the noun mass' can be related to onL\ ~ndividual ph\sicaI. objects, t 1 the noun -3s' 1s duplicated f~s copper' and 'sulfur The noun ratio' is relared t~ Q cmjunctive phrase as a tMyhole. Hence, we obtain the fo1lmiing result for the entire c~njo~ned phrase From thrs table one can see that a gostposition in surface structure does not necessarily cdrrespond to a unique deep case.</Paragraph> <Paragraph position="6"> In the cuu~se of analysis wc muse assign appropriate case labels by considering the case frames of @ha win verb along with meanings of the head nouns of the noun phrases. h gostposition also plays the role of a delimiter which shows the sight b~undary of a noun phrase.</Paragraph> <Paragraph position="7"> The outline of the analysis of a simple ~entenc e 18 FUI follows (1) At first the program look$ Ear a verb in ttlc input sentence, Because there may be embedded sentences which modify nouns in the win sentence, there 1s usually more than one verb in the input sentence. The program picks up the lef @most verb of the sentence (2) The strins before the verb is segmented by laeating pwstposi t~ions (3) Slnce each segment is assumed to constitute a noun phrase, e is passed to the program which anaiyzes noun phrases.</Paragraph> <Paragraph position="8"> (4) When all ttac. segments are analyzed and tt~e hcsd nouns are determined, tt~e program checks each ~~oun phrase against. the verb nakltng wt~ehher a case rell=lrionship will be sat~sfied betweer1 the neun phrase and the verb The checking is carried out right to left starting with the phrase nearest the verb (5) When there are no more noun phrases to be ch~cked, or when a noun phrase which cannot be a case elemnt of the verb is found, the checking is tesmnated* If there remains an intrinsic case slot of the verb which has not been filled, we search for an alppropriate noun to fill the slot from the context This searching process will be explained in section 4, - 43 We determine whether a noun phraw can be n case el nt Q vcrb by 'the following syntactic and semantic chuck (2) The cast) fraws of the verb.</Paragraph> <Paragraph position="9"> (3) The tw,ming of tho 'bond now uf the netm phrast.</Paragraph> <Paragraph position="10"> u.s$ng the second and t'hird hpre of infomution. ?YIP case &dot fillers in a case fx-~nw sf d verb are +reLatfVclv upper cunilept nouns, A sentence 1s considered to be an Phstantuti~n af a case Era*, and the nouns empla\ed will be lower concept nouns of the nouns in the cdse frames, Suppose we analvze the sentence* SHOKVEW -0 FITZV -NI TOUS Lr salt (OBJ) w'jter (IN, RFSLZT, TIHE, etc, wlE, dissolvt (Sowone) dissolves salt In w~ trr.</Paragraph> <Paragraph position="11"> We csn check tuthcthat the sentuncc w~tchcs the case S-IJW of f~XkiL! The check~ng is carformed b~ considering whether salt' is a lower concept noun of 'material' , and whether 'water' 1s a lover concept noun of 'Irquld' Because a case fraw conta:ins only i1trinsi.c cases of a verb, we check extrinsic ones when a noun phrase is found not to be an intrinsic case element of the verb, That is, we check whether the postposition can mark the TIME or PUCE, and whether the noun phrase 1s an instance of the noun 'pldce' or ' time' .</Paragraph> <Paragraph position="12"> me above process Bay appear etraightfoward But sentences can have eeveral ~o~3rble interpretations for the fallowing seasons (1) A verb may hefe more then one usage (i.e., a verb may have several eaae Erams) (2) h po~stpositi&l can indicate more thgn one case. Sow postpcab;itiona can oceus with almost any case; WA is an example, (3) Pk nwm wdified by an ededdad denterlee $8 ai8ualily o casu aloe filler of the e&edded abntem But we may have no syntactic clues as to what case to assign to the noun Xn the event of multiple interpretations the program derives labled inrerp~etati~ns showing all poaslble case relationships be tween specific now8 and wsbs, We cbeasse the intexpretati~n showing the preferable matchin of nouns and case by using an evaluation function below which has been establf ehed empirically.</Paragraph> <Paragraph position="13"> CFN : nuder sf intrinsic cases in n case frcam CIX . nuder st dntrfnsic case elemnts which are filled by the noun phrases in the sentence Ci! number of extrinsic case elements which are filled bv the ndm phrases in the sentence.</Paragraph> <Paragraph position="14"> C3 . number of intrinsic case elements which are filled by the noun phrases in the preceding sentences The value of this fmction indicates the degree of matching between a entesce and the case fzamg of the verb in question, The trial frame which gives the highest matched value is selected We then praeetad to the annlvr;is of the remining strings. If the selection is found to be wrong, duzing the succeeding analysis, control corns back to the point wt which rht* d~cist~7n was ~tade, di9eard.l; it, and C~QOSQR the pt~t tern #!rfrh giuw tt~t' t~c?~t highest ~~~rtching value.</Paragraph> <Paragraph position="16"/> </Section> <Section position="5" start_page="2" end_page="2" type="sub_section"> <SectionTitle> 4.1 Basilq r(pproac'lr to Centoxtual rhslv.iirr </SectionTitle> <Paragraph position="0"> Our view of thQ ~TOE@SS of sentencc understanding is rouphl~ as follows. One reads sentences from left te right and understands then In succession. When he/she cannot understand a serrtence satlsEactorilv, he. she refers back to the preceding sentences ta rabtsln a ke, to understanding If he/she cannot find what is neeed , he/she leaves the questlon pending and proceeds te the next sentence If a phrase or n sentence 1s found wh~ch seems to solve the question, ttlen hershe checks wtr~ther ~t can renllr 18solve the question. If SQ th~ PCII~E;'IICC is prwpexl\ organized into the previous contest and thc question is dismissed In anr caw the pending question is likelv to be disnrtssed as time passes We feel thls process of se~~tence ~tnderstandlng 1s not espec~allv complex. It can be reallzed through an ar~lrlclal lntelllgence approach Whlle we recognize that some klnds of problem may be solved only bv uslng complicated loglcal operatlons, we thlnk most problems In language understanding can be solved by relatively s~mple operatlons Logical operatlons my only be effectively applled on o complete data base In which all the necessary axiom (corresponding to human hnotllrledge) are declared and no contradlctory asloms eslst, In the course of reading sentences, one has - i+6 only partial knowledge about the context, and therefore, his knowledge is not coqplete. However, he can understand the meanings of sentences before he reads through the entire set.</Paragraph> <Paragraph position="1"> This means that one is content with incomplete deductions for under8 tanding sentences.</Paragraph> <Paragraph position="2"> For this reason, we employ rather than logicel operations, heuristically admissible operations whi~h tlBe an intarmedlate term mollory structure and various semantic tckartianehipka deeeribed in the dictionary.</Paragraph> <Paragraph position="3"> b ctsnc~ive of three type8 of memory, Long term mmisr\rr incorpordt~fi knowledge of the world, not considered here. Short term memory is for diate recall of unanely~ed strings under consideration, Inrcrmedistc term memory is limited but contains a structured representation sf recently analyzed strings and strings des analysis, We su rise our approach as follows: (1) Context i.; entered anto the ~ntermdiate term mem~fy, (2) Two k;lnds sf intermediate term memry are prepared. Cxne rs for representing the current contextual content, and the sther is to ~u~ttain pending quce;tions, '&he f~mer is furthe1 divided into the noun wtack (NS) and thc hypothet~eal noun stack (HNS) The latter 6a called the Trapping List (TL) .</Paragraph> <Paragraph position="4"> (3) Contextual. analysis is perf rmed after the processing of each sptactic unit such as a noun phrase or a sentence whlch conveys a mltary idea, (4) NS is organized such that thene words of sentences can be easily retrieved. Here ' theme words' mean the key sub~ects mentroned m the sentences, (5) Sometimes we have to refer to the succeeding sentences in order to understand a seotence.</Paragraph> <Paragraph position="5"> In such cases we do not iwdlately refer - 47 to the succeeding sentences, but instead hold a pending question in TL to be resolved in the courqe aE analyzing th~ pr:,ccr.dit~p ~eflt~ncc8 I 4.2. kkm~n* Strurt~lra tor Cotztextuol lnt&quot;ozr,ttatiun Tha analysis of B scntenrr is primrill p~otmd~d in the semantic descripticw.1 -- case Eram -- oS: n mit~ kcrh. Cr.rnteutual .tnul\si.= ib 111hzit111 pt~~1ndt.d in acctlmulat~d inr o~mnntli~rt about t~olw~t.. T21~ .c7tsj~ct< ~t t~t~voptri that atr ttrr thsws .of thr scnton~~s, and what \~.ls bpt.n plcdicatcd QI thaw car usualkv be charoctexized in tcrms of the r.lQun,s appearing In the sentences, and these ~fFit importa~~t clues tot c~~~textual, rsnal\sis, tJe assign a daffesent LISP atom (produced b\ the LISP funct~on ! gensvm') to each noun which appesrg Infamation about each is entered on the respective propestv list, The fldgs tabulated an Tsble 5, L are used, phrase togethe1 wlth th~s ~bjsst We can retrieve all the descriptions given for an object to which a noun has been assigned, We stack these LISP stom called Noun Atoms on NS and HKS - 48 (A) Noun Stack (NS) When we start to analyze a sentence, we stack a list of noun atoms which are a~~igned to the nome in the sentence.</Paragraph> <Paragraph position="6"> These noun atoms are reordered ascarding to their degrees of importance, NS has the construction ehown in Figure 4.1</Paragraph> <Paragraph position="8"> TQ decide how importme a word is, we use the following heurlstlcs.</Paragraph> <Paragraph position="10"> In Japanese a them word 1s often omitted or expressed by a pronoun rn succeeding sentences after ~t appears once. In other words, the bord which IS omtted 0s expressed by a pronoun 1s an important word for the understanding of a sentence.</Paragraph> <Paragraph position="11"> (2) A them word ~lav apyselar as &quot;dubject1' in the surface stl~~cture. To emphasize a word whxeh is OBJ-case in deep case structure, or to de-emphasize a word in the ACT-case which is not worth mentioning, the pasgive voice may be used. This places a stressed word In the subject position of the sentence whlch would otherwise appear as object or xnd~rect object (3) The xmportance of a head noun in a noun phrase is greater than that of other nouns.</Paragraph> <Paragraph position="12"> A afqle example of ranking by importance 1s shown m Figure 4.2, zinc appears in all the sentences and is the theme word, Repinn~np of the annl~$is of S1: ((Nk IT3 Eu'2 N1)) End of the a11~1vsI~ 51 ((U4 N1; W 3 K:)) Br@nninq nf the ,~nal~sit. 13t S2 ((h'51 (HA N1 N3 N?)) End of the ,mnL\sis of S:! ((W4 N5)(N4 Nl 33 E32)) Beginning of the anal~srs of S3 (NIL (N4 W5) (N4 Nl Y3 ~2)) End of the analvsis of S3 ((W4) (34 N5) (W4 K1 N3 K2)) Flgure 4,2 Changes of KS (B) Bypothetical Now Stack (HNS) We fir~t show examples which cannot be properly analyzed without HNS. In these two examples, though the deaons trative KONO (the, this) is used, the object referred to does not appear' explicitly in the precedinn sentence. The object referred to is produced as the result of the event which is oxpreared by rhe preceding sentence. As mntioned before, we append to case frama in eha wrb dicti~nav descriptions of any ~bjects which my be TQMSU (dieeolve) ha the ease frame: ((ACT huaun) (0BJ material) (IN liguld) ) and this case fratne has the addxtisaal description:</Paragraph> <Paragraph position="14"> The symbol $ in this description is a LISP function which fills the - 59, sg,ecific ease elemnts indicated in the arg nt the currant malioat$a~ of the ease f ram, fie sentence dssociatad with the above caos fr msn~lts in aha FolSaqdiap f n tearlppset~t $,an 2 a new object, a solution whose soavant is vstattl and whose golute is; salt msults, Wa repmsant this newly produced object in HWS instead u$ K$ for eha &ul%wtiting two msk;tlnrtl 1 b the descfiption ia baaad un wcettabn knm*ledg~, fe is likely, bur not necessarily so that the object is produced in the redl world, If we find some descriptions sf this derived object in the succeeding sentences, we wiU decide it really ensts md transfer the reprasentstnon from HIS to NS, 2, Because the newly pssdwced object is referred to in the swcceed$ng sentences sumtims by different wax ds or by sjmtar ti~:~l.v different form, it is soriieaient to stuck them individusllv in WgS, 4-3 Estimation 0% the Omittad 'Gk~rds In the analysis of s Jspmesc sentence it is isp~rtant to supply omitted words drawing from preceding or succeeding sentences, TB do this we must be able to: 1, recognize that a word is omitted and 2. search for an appropriate word to fill the gag Our contention IS that an individual syntactic unit such as a noun phrase or a simple sentence conveys a definite idea; a noun phrase may designate a certain defirdte object, a concept, or whatever, and a simple sentence mv describe a defin teievent. In order that a simple sentence describe J definite even, each intrinsic case element of the ease frame must be - 52 epscidicd by particular abjecte, We can detect an omitted ward by searching f~r unepecified case elomeete in 4 case frame.</Paragraph> <Paragraph position="15"> ~oreoves, we can guess from tha ceiaiee Xsma what kind of nouns ahauid bo supplied tu fill. any gape, In thi~ mnnor wa can detect and supply omitted wards by using the @mantie d~ser%~~ia3es~ in tha dfctisnary.</Paragraph> <Paragraph position="16"> (1) bitead Wosds in a Singla Sentence men we have finished th~ %anaLyei@ of 8 sialplc zrrntence, FM chack w2rothar there raaata come intrfist~kc cages rw be specified. Tf there remain gme, we search Erar appropriate ftllsrs in the preceding aenteraceta, The ~sarching psocess ia carried out in the forlaw lng way, (i) We aeasch through HNS first, because an object newly cseeted by the preccdqw event is sften the theme object of the present event. (ki) In Japeneee, idenrxcal case elements in succeeding sentences are apt ta be omitted. So the pteviow sentence is seasched Ear elements having the erne sea%le relotioar the oaa under consideration tks~tdgtl MS+ {Eii) If thc above psocassas fail, then we ehack the wzarda i, NS rnt al thg mrd~ that have appeared in the three previoua sentences one-bp-one until we Eind remntisally edmhsaible ward.</Paragraph> <Paragraph position="17"> (be If we cannot Eind a suitable wosd, we set up a problem in the trapping hist TL (mntioaed in the next section).</Paragraph> <Paragraph position="18"> Same results of the psa(ceesing are sh~m in Figure 4.3. (pgs, 54-56), (3) Oaitted Ward in a Bow Phrase A aam ts classiffed as either an entity word OX a relational mad. &st noma have definite maning by theraselves, and are regarded as entity words, Mowever, saw. kinds of now have relational meaning. That is so say, they have slats Za their maning to be filled fa by other words, in I=* W+BMU -DEl MESSMITE, NUSMI, KA~SATSUSURU, put in gae burner (INST, NTHOD) hent mkt eabaerve waning: (Sowone) puea naphthalioe in the test tube.</Paragraph> <Paragraph position="19"> **Shou& the third kod fourth seateanr als~ Ru mrkem, they are pr~wrhy filled in, Eel okr taiaed.</Paragraph> <Paragraph position="20"> Rgure 4.3 continued order that they exprese definite ideas.</Paragraph> <Paragraph position="21"> Sometimes a relational noun is used alone in a noun ahxase. In this case the relational noun must be semantically c~rmecttad with lother word8 which are o~tted in the prelisert~ noun phrase. Such cxamplea are ehown in Figure 4.4 balaw, (11) rov -Q NESSURU TDKI XRQ -GA HE~S~U , sulfur (OBf, TOBJ) heat when color (SUBJ) change meaning: When (eowone) keate sulfur, the color changes. The phrta~e '$RO -M is a noun phrase but it h8 incomplete collcar (s'tSf3J-l by itself. We can easily understand the color means 'the color of the eulfuat, (23 Efl$AH -0 -HI hydroehlorbc acid (OBJ) teat tube (PUCEb TIME, etc .) 2Occ zmu.</Paragraph> <Paragraph position="22"> put in waning: (Sasde~n~) puts 20cc of hydrtachloric acid in a test rube, &The word 2Ucc i put in a separate position from ENSAH fiydsachloritl tac%d) in gha oentence, Zr, howewr, specifies an attribute of the acid, Vo;&m, 4s the fdaab step in the analysis of a noun phrase, we check whether there remain relational nauos which have no definite meaglng. If found, we search through NS for words whrch are suitable to fill in the slots of the nome, The ecarchihg process 1s che same as for omitted words In siaple seatencea, Sometiaes the omitted words exist in succesding sentences, so we cczn set up a problem in TL, if we cannot find an appropriate word in the gracedhng aentencea (3) Detailed Description of the Trapping List (TL) lIost enaphoric exptessiom and olmittrd words are well onslye~d by searching throwgh the paceding sentences, Howav~r, ua need ametims to refer to succedine, sentences in order to anlrlyto a $anten$@ properly. Zhn sentences sk~aum in Figwrc 4 5 dre eesxnaples, manhng: - - - - the eoapound which is heated and whose state changes(2) ONl3.I -0 Z&quot;3&quot;SEI -NI SHI, ATSLbRYOkC -8 temperature eonstanf (PLACE, RESILT, ete 1 )OBJ) increase when waning: When the t.i.mperiltwe is kept constsqt and the premure is bnereaaed, tkr volume of gas - - Figure 4.5 Examples here omitted vclrds appear in succeeding sentences Because the precedlng sentences have already been analyzed and both WHS and NS have been set up, i.t is easy to refer to the precedlng sentences On the other hand we cannot immediately refer to the succeeding sentences if this 'is called for.</Paragraph> <Paragraph position="23"> To solve this problem we set up a trapping list TL, The bas ir organization of TL is show in Figure 4.6, h trapping elemnt 'is a triplet and corresponds to s pending problem.</Paragraph> <Paragraph position="24"> When we cannot Eird an appropriate word in the preceding sentences for an omitted word or an anaphoric expression, WE put s new trapping element in TL. At this tdme the first of the triplet, N, is eet to zero, When a noun phrase dn a succeeding sentence is analyzed we pick up nouns isom the noun phrase one-by-one end check whether the present noun can resolve a pending problem in TL by evaluating the function Fl la the trapping element.</Paragraph> <Paragraph position="25"> We have defined several LISP functions far the functdoa Fl. These dunstions wark aa follows.</Paragraph> <Paragraph position="26"> (t) They check whether a new at hand Bsn salve the1 problems in TL, (ii) If it can do so; they update the dlta (for example, iE the function F1 is the function which searches thp words in TL for fdling in the? omitted case element, then the function will put the present noun in the case frame), and return the value 'DELETE' Then the system. will delete the trapping elemnt from TL.</Paragraph> <Paragraph position="27"> Ciii) If it cannot do so, the system adds 1 to N, the f icst element of tbe trapping element. Wen M exceeds five, the trapping element is deleted frorp TL. That is, it is decided that the problem corresponding to the trapping element can not be solved at ell. Before the deletion of a trapping elcmpenr its third element, the function F2, is eveluated. Thus far F2 has cnlJ b~an used to provide default values to sllowu some intet pscrsrlon 1 1s p~ndinp By using Pbp idea of TL, we can t;eparate *ridas checking wchuuisari fro& the win program. They can be invoked crutanwtitaliu whcn o noun &ppatn in n senten&@, The idea of TZ. msedles thee af 8. Z'harniak;'~: 'dsw4rtt (J197211, When his rryateffi sncowntars a coctoin word, far cxerirlc, 'pi~ bank', it cuoeta~ e demon which tries to catch from the succeeding sentences any word (e.g., money) related to the key word. Ke fear that unnecessary knowledge wilt clog the system with e 'combinatariel explosion' resulting from the proliteration of demons, Ous trapping elemat is gut in TL only temporarily to compensate for any missing elements to be retrieved from succeeding parcs, Hence the uli~tlcessary psoliferati~n af elements tgsv be avoided, 4-4 Processing sf haphosic Expsessi,cns In Japanese anaphora is exprwsaed b\ using the articles KONO, KQRE, sb nt~icl~ correspond roughl~ tm ' the', ' this' and ' khese' in hpltsh. prflnoun KORB is used to designate o sinfile object in tha prereding sementes, and the pronoun KOUU is used to designate plural objects, The article E;OMO is used as a constituent of a noun phrase. Though the articles in English modify the Elrst succeeding noun, KBNO often modifies a no&? at some distance. h example is given in Figure 4.7.</Paragraph> <Paragraph position="28"> noun noun noun KONO SBIKm -NO NW -NO MU this test tube 1n copper the copper in this (inside of) the test tube this copper in the test tube Figure 4.7 In thie exawple there ate three nouns following the article which can be wcfifled by it eyntactieally, We must decide the preferable modlficati@ pattern by using c~ntextual informention, In the analysis af a noun phrase, we scan the words one-by-one from left to right, When we catch the article 'KDNLa, we put it fn the tewarasy stack. The wosd will then be checked to aee *ether it can modify a noun in the fallowing noun phsasa. Vhea.1 we scan rh~ now SH31aNUN (tset tube) in Fbgure 4,7, we check whet'taer the object indicated by it was elready mntioned in the preceding sentences. 11 it was, then the article RON0 is regarded as modifying the noun ' test tube'. If not, the article is stacked again, In this way the article will btz checked against rne nouns in the noun phrase until the noun modified by The article KOWO is used in the folhawing two ways: {I) SANS0 -W ARU MONO SANS0 ---- null axygen (SW, ACT) exist oxygen (OBJ) mere ia% oxygen The ovgeaa - - - - - %%a a~un SM$O mdfffad by the artdele HONO is the same entity noun which appears in the first sentewe.</Paragraph> <Paragraph position="29"> (2) %US0 -a UU, - KONO TAfSEKZ -0 volusle (053.51 There irs oxygen. The volume of the oxygen - - - - - Zn thfs cae KONO alone desigaates the entity noun SAHSO wh:ich appears in the first sentence, This usage is permitted only if the noun modified is a selotioaal rr,wusls If the now has only a relational maning, the second usage appears =re of ten thaa the first, me 8aa9ashg deecsiptioas of articles and pronouns like KONO are prasotdully e~reased by LISP functions.</Paragraph> <Paragraph position="30"> The functions in the dicti~nary will - 61 be evaluated if we find such words in a sentence, Tha fmctian lass RON0 operates in the following way, (1) Mchech is mede to see if the sucq~rding n6un is icletlonrl. If the noun ha;% only a mlarftan&l warzing, kt 89 firsti sswumd tl;tat the artieka KONQ is cf the second usage and we go to step (3). Itf nat, wa p to gtep 3621, (2) Tha Elrs t usage of KONO has thc following three veoir'ties. (i) SMSO -GA ARU. KOHO WSO -0 - - - - null There ia DV~P~. The ~xygstl - - - The now mdifiad by the article is the same noun which appears in the preceding sentence.</Paragraph> <Paragraph position="31"> (ii) SANS0 - RRU. KQNO KITAX -0 These is oxygen &quot;IThegm----The noun ' gas' mdifled bu the alticle is rn upper concept noun of the referait now 'oxygen', (iii) SANS0 -TQ SUISO -0 KONGOUSURU, RON6 KBNCBLKlTAS -& oxygen and hdra&en (PBJ) mix gas dxtwre (OBJ) (S~mone) nixes oxygen and hvdtogrn, The gas mixture - - The article modified a nodnalized form of the first sentence. The first sentence lgstantiates the case frame of the verb 'mx' FJe ewduate the NWS description of the case frame and obtain a new lnierenced object 'mixture', whose elem~ents are the oxTgen and the hydrogen. The noun XONWUKITBI modified by the article is a lower concept noun oi the infesenced now (mixture) in ENS.</Paragraph> <Paragraph position="32"> According to these three varieties, we provide the following three check sautines. The order of checking in shown in Figure 4.8 Ia there in the li~t the earn noun cre the noun modified by KONO.. (cherlr. 2) 18 t11ete in the liaat a lower concept noun of the noun modified by KONOu (check 3) Its there in the MMS list an upper concept noun of the mdified noun, ad are its patopert*s consistent with those of the wdified noun, Zf we cm Eind a naw which satisfie& one of these three conditions, we decide that it is the referent noun. If we cannot, the function for KONO returns the value NIL.</Paragraph> <Paragraph position="33"> (3) If the noun which fQlLaws the article has a relational meaning, the mkaninp descriptiwn of the now ha@ dore wh;d eh must be filled in by other wotde. What kind gf nsws is preferable far the slots ia described in the waning dee~riptiran~ We csearch ;tn MS and HNS for a object which satisfies the des'csiptian, For example suppoee the input is SmSO -GA daRU. KONO TAISWL - - oxygen (ACT SUBJ) exia t volume The nswr TAXSKI is an attribute zlxolfnw So we look for a noun which may have the attribute and recognize that oxygen is appropriate. hother example is -- GA MU. KOMO -NI - - test tube (ACT SUBJ) exist in (PUCE, RESULT) There is a test tube, In the(test tube)- - - null The ncrw WKA (in) is a pzeposdtiurtaS, noun which require8 B container1 or 'liquid', We em easily racopite the test huleict se s Ssvar concept noun sf 'clontainar, ' Therefore wc aasum tha word ROgO is uad f~r the test tube, Xf we find no such nouns, wet gupposc that the axtiel@ KOw i~ not of thle eacpbnd usage But of the first. So ua will ga tc step 2, The pronoun K03Ui (this, it) is used in sonteoccs ss e rasr cicrnt. case [fra~~ description of tha wrb in a aentetlcu. Tha pustpositivrl attechad to the prondun indicates a set of possible cases, By t&iog fro& the frams the cases which belong to the set; we can obtain the semantic descriptions which are satisfied by the object des~gnated by the pronoun So we search through HMS md NS for an ot3 ject which : atisfies the descsiptiens, Consider There are 500ee sf water, In this (water)(someonr) puts in 2 grams of salt.</Paragraph> <Paragraph position="34"> The set of possible cases for the postposltlon NI is (PLACE, RESULT, TIHE, BEWFZCEHT - - -1, and the case frames of IRERU (put :in) have the cqse PLAw-, We can predict that the pronoun KORE (thls, it) fills the PLACE case in the sentence. The semantic description says that a lover concept naun of container' or 'Ilquid' is preferable as the PLACE case of the verb IRERU (put in) .</Paragraph> <Paragraph position="35"> The object 'water' , which is a lower concept. man of ' liquidv , is found in NS, and is detemuned to be the object designated bt the pronoun We have some other pronouns and articles in Japanese which are analyzed in ehe same way. We provide different LISP functions for different pronouns and put them in the dictionary definition8 of these words. T, Wfn~grad treated the saw problem in hie excellent system SkSRDLU (1971; 1972). lbwever, the world which his eys tern can deal with is very hjdted. Sn order to construct a system which can treat a wider range aE ecntcncee, the gystem should be equipped with the schema representing the re$~ei-srar%h$ps between event@ and abject (an event my iwly tl.u$ occuttrarentllc of new objects et changee in the propertie$ of sbjects).</Paragraph> <Paragraph position="36"> In real. world eeatencee, there exists mse complex ghenoena about anagheric expressions and ad~~ions of w~rd~ than those treated in SHRDLU. We do not claim that aut eyetern cm treat suck .c&pl.ex pnensmna, but we hope that our system can be evolved to cover such phenomena by man$ of combining contextual analysis procedure with seaantbc descsiptlons of words, kn the prevslrrus ~ections we described the sezwntfs and c~ntextual maJysPa pracedure af our @y@tQms In this seetLm we explicate by using sentences hw these fmctioaal wits are organized in order to analyze fai sky co~~~plex sentences.</Paragraph> <Paragraph position="37"> (I) Suppose the input sentence is &SHmPSAETE 'SAZSEKI -a MEW-SURU WKI -NO SAHSO -NO be eonpressed voLume (SUB5 ACT) change t I= OW gen CSoaane) obgervee the stare of the oxygen when it is compressed and the vdum (of it) changes, migsusesr the! pressure, and expresses it by a graph. The sentence is analyzed by the fo1law;ing steps.</Paragraph> <Paragraph position="38"> 1 T'he program first tries tci find the Pefemst verb, am! anslyaes the rlausc governed by the verb. T%E sentence ASSHMFKWSWMTB (ba ruaprrsaedl is tknaly~ed first. Thia osntence llaa lan irm~uler ~tsuctwka in the RO~IBF that them are no aq;lticit wsa @laments beEow the '~&quot;a?:rL kPB, vase olaw~nrs are omitted in thia aentan~.Fa: By ellacking ttto dnh$ectfuza QI the verb (ASSHUKU-SURU (to rampress) ---ASSHWUS83: (to be c~@presscdl). w re.e\.omici. that tho ecntencr is in the passive voicr. Tho lakical drsrrLptiaz~ of tile verb in the word dictionam indicates that it takes twc intrinsic rases, theat is ACT03 and QBJBCT. fn a Japanese sentence cspeefab&l; in thc field of chemistry, the case element ACTQR is apt to be neglected. Therefore we adopt a dummy filler fa the ACTOR to represent the author of the sentence or sow other human being. As there are no preceding sentences, we cannot frll in the OBJECT ease immediately. So we set up the pending problem in TL which will watch the analvsis of the succeeding a tsinps to fill the gap</Paragraph> </Section> </Section> <Section position="6" start_page="2" end_page="2" type="metho"> <SectionTitle> 2 The clause TAXSSEKI-CA HENKA-SURW will be analyzed next The </SectionTitle> <Paragraph position="0"> verb HEN&\-SURU (changed requires uln$v SLWJ case Thr! puseposfrl~lrl Gb, attached to the; noun TAISEKLCI (v.sLwm) paasiblb LiapLies the case SlrRJ. Ttlc noun TAISEKI: 1s a Irstver concept noun of 'attribute', which stjtisflcftzs the semantlc condition for the case element. So thls sentence is analyzed :in a straightfoswasd manner, However, because the noun TAISEKI 1s an attribute noun, we must find the corresponding entity noun.</Paragraph> <Paragraph position="1"> Tkqt IS, we must identifj the object whose volume is being referred to. As we cannot find such an object in the preceding sentences, we set up a pend~ng problem In TL, I3.1 checking the inflection of the verb WENUSURU (change) and noting that it: is iwwdidt ely followed by a noun, it is recugnized that the sentence is an embedded sentence modifying khe following noun TOKI (time, when), Fu'e then - 66 connect this caentential part wit.r the norm TOKE by using the relation SMOD (WDif ied by a Sentence) .</Paragraph> <Paragraph position="2"> 3. Wen we analyze the next chaau~e, m3, -NO SMSO -NO JQWAJC -0 WSATSWSURW ti- QW$Qn $rate (OBJ) observe when ua first perfor. the analyais of the norm phrase TOKI-NO SANSD-NO JOVTAI. pemisaible becauee 'oxygdn' is e lowet conlcept noun of 'nrateriel' , and can be lodified by a word which designates a speciaii point of time. The noun mKI (ti=) ip mciified by the e~nteatid part analyzed at step 2, and deaf gnatee the tfm wbn the event expressed by the senteatid part occurs. The cedinotion af SWSO (owlgen) ad JOUTAI (state) is also permissible. The nouns T.OKI (time) , SmSB (oxygen) and JOUTAI (state) in the now phrase activate the trapping elements in TL. The noun SBLNSO (oxygen) aatissfiera the tloradftlcsns sf the two trapping elements set up by s tepa 1 and 2. That 94, 5M58 (ovvn) fills in the case OBJ af the first clause, TAISEKZ (vstum) in the wecsad clause is wgarded as the vdum QE the oxygen in the @u~tent c%&u~a.</Paragraph> <Paragraph position="3"> 4, The next rlawe AmmYOKU-e;) SOKWEISHI presents no new preableus. Warever a referent for the noun ATSURYOKU (pressure) must be found. 'oxygen1 kw the preceding sentence is easily fomd to satisfy the conditions for having the qualf ty ATSmYOKU (pressure).</Paragraph> <Paragraph position="4"> 5. The remaining steps follow along sirmlar lines. me results of the parsing of the expressawn are shown Figure 5 1, (pg. 68).</Paragraph> <Paragraph position="5"> (2) The next example sh~ws how UWS is used. Suppose the input sentence is (1)- Input sentence: AS SEWUSMTE, 'lAISB#P -GA URZJI TORE -MO be eomprassed velum (ACT SUB.!) lchans *an SdWSO -NO JQmA1 -0 UTSBHIt, SOH&quot;L~ kaBml9KV -0 a xy gi$n ststre (QBJ) obaex~e the pna%pruz% (t7bJ5 -wing: (Sawone) obserws the state of oxylpn which ia euqrc.sssli md whose volwmm changes, (Sumanel wasurns pte0;st3m and mgseaerrts it as n araph, Figure 5 3, SmSO -TO %MSO -0 KOHaWZ, RON8 KOMCQUKITAI -NI hydmpn md oxyen (OBJ) dx the gas mixture mu ~7x3 %MUBFSHIs KZU -GA DEKf RU, ftrs (if, when) explode water (SUBJ, ACT) be mede mrf dxcs hydtpgan md oxyen, and fires the gas mixture, then (it) e~lodar and watar rcltauhts, Ihr! Iollaving stapr are petforrd, 1. Whan the analyofa at the Eiree clause SVISO-M SANSD-O KOM-EOUSiiI Ir caplcte, tha cue frrree of the verb KONWUSHI are ingtantieted. The S etpreeeioo of the epee fram which obtains the highest matched value iaf deterdncd. As the r$sult a new object ' dxture' is created and the elewnts of tbc plixture ere hydmgen and oxypn. This newly created object is put fnto HHS, 2. The row phrase RONO KOSCOLKITAT-NZ (to the gas mixture) in the clause is mdified by the aaaphoric determiner KONO (this) which requires a referent. The now KONGOURlTbI (gas dxture) 18 a lower concept noun of 'crinture' having aa components gaseaua objects. We search in the HNS and NS and find the object 'aixrurce' in HNS whase eleplents an the i~rdsagen and the 3. Ihe object 'gas illixture' ia the theme of the succeeding senteaces. f t fills in the o~itted case ACT of the third clause a~d FROM ease of the fourth clause. Figure 5 -2 shows the result of the parsing (see pg. 70). Table b. I below showa the score ob taioed by applying our parsSng program to the sentences in a junior high school chemistry textbook, p) Input sentence:</Paragraph> </Section> <Section position="7" start_page="2" end_page="11" type="metho"> <SectionTitle> RON0 KONaWJTAI -HI </SectionTitle> <Paragraph position="0"> this gaa~ dxtuw (QBJ, IQBJ, PUCE* ste. 8 &,mi ik7~m *Q11 maning: IF (sna~ne) axes hydr~pn and oxyrn and Lmita. it, rh the mazaes uioh~ntl~ ad water hs pmdwe, TABLE 5.1 Successes and Failure$ Schema, V f WHlCLUS LON the our interpretive procedure as follows: [a) &quot;%lrrou@ the use af gra tical case we describe patterns of activity in the wrb dittiranaq. The descriptions also contain information as ro haw activities are c~nmcted with each other and haw activxties change abjects.</Paragraph> <Paragraph position="1"> Eb) The: meaning clescriptioras or nouns am based. upon the upper and lower e~ncapt relationships and attrfbute value pairs. Some kinds of nouns are regarded as having relational meanings. Their meaning descriptions arc @idler to those of verbs, adjectives, and pt~positions. By using these deecriptions we can analyze fairly complex and long noun phrases where there ate few ayntactir clues, (I) We do nat use log%cal expsessrons to represent context Contextual informtiara is repreeented in the Eom of what we call intermediate tesm memsy. This in comlbinatlon with the semntlc descriptions of wards be3 errabled L~S to perform efflclent analyses dependent on contextual information.</Paragraph> <Paragraph position="2"> We have developed a programming language which makes 3 t easy to ws:ite gw~~~xs for natural language ad to control the analysls procedure. - 71 By wing this languhge, we cah i+neor;porate naturally sewntbc and contextual analyses into syntactic analysis, We do not aced a large &nd involved proernza which it9 rs~lponaible for the sawntic intamraeotian ~f tha output given by the syntaetle analysis eowonent, Im~tead, awlysera, We have obtained faf rly gaod re~t~lts wfth our npprasrtt', Thpia contextual analvsds psggren on the other hand can treat unly local c~jntrxta, In ordet to treat mm gloBaS, contexts, @e lecl the foll~wing b~r~wm'~t%i (i) Fh must provide our system with an approgsfatesik~c~ c~rmspanding to human long tern memory in order to represent the state sf the world, 'She system naus t haw fr.timst;wearks to express spatial relationship$ among objects time relationships awn8 evr.T7rs and so on.</Paragraph> <Paragraph position="3"> (ii) At the present stage we have only one .relationship CON t~ connect one activity with another. Wdwever, human knowledge of the world attlsnm~daeas varitaus, kinds of relsticansfzips among activities, such as cause, purposs;, mason, ete. These relationahips nmv play an S~purennt role not only in the analysis of sentences, but also in the ~nfercnce processes in answering a question.</Paragraph> <Paragraph position="4"> (lii) fie descriptions of verb meanlngs using case work rather well far analyzing verb-centered sentences. However, the results of analysls depend on what verbs are used in surface sentences Hence, the sentences which convey the same meanlng but are expressed by using different verbs may be transf omd into d-if ferent internal representations This is a serious drawback when cons tsucting ques tion-answering systems or other kinds of intelligence systems. In order to avoid this drawback, we attached a set - 72 g rules to each case ftroe siprilar to the descriptions used at hsys;te@ (1913). We feel, howevesr, that thig wthod is rathsr rvku.rd urd char deeper structuraa ehould be employed (similar to ' carc@ptU11 dependency' propoaad by R. C. Schank f 1973r; 1973h). (iv) In order that 1 ryeetrtn be able ka cumwlcata with paaph in a fLodbEc md nitural mntnsr, it t bs &la to drrim infemtrcas from incoqlece data baa... Thenrforu we mat: design a procedure othar than the mi fotr prcof pmcadurc &uch is the nraolutfon proof pracedum . (v) It flP nem~~aq SO apply out mtbed in fields different from chad~tsy and to reart *athat our oa tie dsecrigtion mthod should be &m@d or Bat.</Paragraph> <Paragraph position="5"> I&quot;k-rsse are y ~chslars &as are interested in aing cme structures as a mpresentatlon of natural languap utterancee, 0, Bruce (1975) offers a good Gurney and a unified point of view in favor of case sys teas. We also Baliem that the erne system ia a pradaing apptawh ts the repmaentstion of amiogs ia natural imgurga. Fvrthpr va baliave that the idce of case giwar ua a weful tad ejot mptepsenting kwwledge of humn beings. &quot;Sfre Ida= reported here cam from oly h;ources. We would especially like to tbmk It;, T~lxxaka; H. Tanabe and A. Tereada for discussions of cases and semantics Aha our thanks to D. Sdth for hi8 valuable suggestions; speeial.thanks to Linda Arthur for the preparation BR~ typing of the muscript,</Paragraph> </Section> class="xml-element"></Paper>