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<?xml version="1.0" standalone="yes"?> <Paper uid="C92-2083"> <Title>Structural Patterns vs. String Palterns for Extracting Semantic Information from Dictionari~</Title> <Section position="2" start_page="0" end_page="0" type="metho"> <SectionTitle> 2. Semantic Relations </SectionTitle> <Paragraph position="0"> The semantic relations that are needed to provide a semantically-motivated analysis of the input text have not yet been enumerated by anyone. It is possible that this is due to tile absence of information on a Large scale that can be used to test any hypothesis of a necessary and sufficient set of semantic relations. Semantic relations associate a particular word sense with word(s) extracted automatically from the dictionary, and those words may be further specified by additional relations. The values of the semantic IFor example, the grammar for English was used, without modification, to parse over 4000 noun definitions. With a parser that forces an NP analysis, over 75% of these definitions parsed as full NPs. These are very good results, especially since many of the remaining 25% do not form complete NPs and so were parsed correctly.</Paragraph> <Paragraph position="1"> ACRES DE COLING-92, NANTEs, 23-28 ho~r 1992 5 4 6 PROC. OF COLING-92. NANTES, AUG. 23-28. 1992 relatious therel0re bave nlore ocinteEt Iban binary featnres and are llot abstract semantic prilnitives, bill rather reliresenlations of the iinplicit links to other senlauti(: fralnes.</Paragraph> <Paragraph position="2"> An example of a semautic lelation than cau I~ ideutilied in the differentiae is LOCATION-OF. The deliuilion of 'market' (LDOCE n.l) is expressed its follows: &quot;a building, sqnare, lir open place wllere pcxlple meet to buy and soil g~lds, esp. tkxld, or sometimes animals.&quot; As we will show later, it is possible from the structural descripti(ul of this definition to extiact the followiug wilues for the semantic relation I,OCATION.OF: Accm>diug to this semantic fEaine, the vellis &quot;meet,&quot; &quot;buy,&quot; and &quot;sell&quot; :ire related as LOCATION-OF to the noun &quot;market.&quot; AIIbough the words extracted from the definitions are not di~imbiguated themselves according to Iheir senses, as nnlcb iulbrmation as possible is iuEluded in the semantic fraine as the definition being analyzed provides, lu this example, the word &quot;nicer&quot; is further specified by a semantic rehition HAS-SUII.IFMT that has &quot;people&quot; as its value. Also, since the verbs &quot;buy&quot; and &quot;sell&quot; me conjoined, bxlth verbs have a HAS-OBJECT relation with all the syntactic objt~ts identified in Ibe analysis.</Paragraph> <Paragraph position="3"> namely &quot;goods&quot;, &quot;food&quot; and &quot;animals.&quot; Semantic infomlalion ~lf this type is necessary, fin&quot; example, in order to automatically interpret noun conipomlds. Given the (partial) semantic frame above flit &quot;marker' and given tbat &quot;vegetable&quot; lias a purpose relatiou to &quot;food&quot; (infornlation also automatically derived by applying structural patteras 1o the dcfinitiml text), tile uoun conllxiund &quot;vegetable market&quot; is iuterpreted autonlalically as: &quot;Market is a location for the purlio~ of buying and/lir selliug vegetables.&quot; (see Vanderwende 1992) Examples of other semantic relations that were required to interpret noun compounds are: SUBJE(TF-OF, OBJECT-OF, FOOD, MATERIAl:.</Paragraph> <Paragraph position="4"> TIME, ttUMAN, IS-FOR. LOCATION-NOUN, MADE-OF, CAUSED-BY, CAUSES, MEASURE, and MEANS.</Paragraph> </Section> <Section position="3" start_page="0" end_page="0" type="metho"> <SectionTitle> 3. Strnctaral Patterns </SectionTitle> <Paragraph position="0"> Tile acqaisition of seluautic relalimts IiOltl online di/Dliouaries lU(xseeds by applying patterns to the sttut:tm~ll descriptions ot the det trillions und cxanlplc sentencl:s. The pattetus emb(~ly kuowledge of which relati(nm cetlaiu let'utfillg elelllelltS aud COllSllqlctioits convey ill tile tit.lille)el (if the dictioimry. Fro inslance, the tel;lieu PURPOSE is coaveyed in Italian by life phrases: &quot;con lo/allo scope di,&quot; &quot;al fine di,&quot; &quot;p::r,&quot; &quot;nsato per,&quot; &quot;alto a,&quot; &quot;the serve a,&quot; and &quot;utile a&quot; folltlwed by a noun phi;so itr au infinitival clause.</Paragraph> <Paragraph position="1"> In English, this same rebiti(ulsllip is conveyed by qnite siiuilar phrases, also followed by a nlnm llbr;ise, luesent pailicil)le, m itdiuitival clause: &quot;l(ir (tile) liUrp~lse(s) o1,&quot; &quot;lku,&quot; &quot;used hit,&quot; &quot;iilteuded for,&quot; au(I past palticiple followed by &quot;to.&quot; Alter locating the llatLelu within the deiinitiou, the Ilue exlractioll process cousisls iu identifying tile values to bc ass(x3ialed with tile Seluautic tel;lion detected. Typically the values (if tile semantic relali(ius fire the It~lds of the pattern itselt tit&quot; (if the complenleul(s) in let ms of slruolnlal patterns, iir the next conteilt word(s) in tetras (if string patterns. H(lwever, exlracting eveu hi(ire spccilie informatiou trum ttle differentiae, fiir example that lbe verb &quot;nicer&quot; has &quot;t~tlple&quot; as its subject when it is die I,OCATION-OF &quot;lnatkol&quot;. also inv(llvcs the ideulilieatioll o1 fuuctillual atgnnleuts el verbs and ill the ease Il|' nouns, identilicaliou of adjectives aud &quot;with&quot; clmiplements.</Paragraph> <Paragraph position="2"> A simple ex;nilple of a sll-uchnal l)atteiu is llle liatielli lllal extracts Iho semantic tel;lion PURI~degJSE, fioill the itaKsell deliniti(m text. The pattettl can be palaplnasexl (in pall) as: if Iho verb &quot;used&quot; is Faist~ni(rclilied by a PP with the preposili()n &quot;for,&quot; then e:,.tract file head(s) (if that I't' and return those ;is the vah\]e of the PURPOSE relatiou. If Ihe 1't> has a verb ;.is ils he~<ld a\]id an OI\],JE(.+'i + attribute, llCiUlll tile llead(s) (if the Of IJECT as the values el a 11AS. OIIJECT relation; and if it has a SUBJECT attribute, return tile head(s) tit&quot; the SUBJECT as the wdues of a ItAS--SUBJI ~(Ti&quot; relation.</Paragraph> <Paragraph position="3"> Cunsidor the relevant sectiml of Ihe parsed (leliniti(ul (if</Paragraph> <Paragraph position="5"> ACEI~S rOE COLING-92. NANTES, 23-28 AO~t' 1992 5 4 7 Pitt)t;. ot: C{3LING 92, NANTES. AIJ(i. 23 28, 1992 The parse tree shown above 2 is but one representation of the structural description of this definition. Below is an excerpt of the record structure containing the functional information for tree node PPI above: phrase in Figure 2.</Paragraph> <Paragraph position="6"> Following tile structural pattern for PURPOSE, we see in Figure 2 that tile VERB 1, &quot;used&quot;, is post-modified by a PP with the preposition &quot;for&quot; and so the base form of the PP head, VERB2 (&quot;store&quot;), 3 is extracted as the value of the PURPOSE relation associated with &quot;cellar&quot;. In addition, an OBJECT has also been identified in the structural description, namely NOUN2, and so its head &quot;goods&quot; (ill this case, tile noun itself) is the value of the HAS-OBJECT of &quot;store&quot;. The result of this pattern will be the partial semantic frame for &quot;cellar&quot;:</Paragraph> </Section> <Section position="4" start_page="0" end_page="0" type="metho"> <SectionTitle> 4. Inadequacy of String Patterns </SectionTitle> <Paragraph position="0"> Some patterns to identify semantic relations are relatively trivial and can be handled by string patterns. For example, no matter where the string is found in the definition text, &quot;for (the) purpose(s) of&quot; as well as &quot;con lo/allo scopo di&quot; always indicates a PURPOSE relationship between the definiendum and the head of the phrase (noun or verb) following &quot;of/di&quot;. Markowitz et al. also discuss patterns at the string level, based on defining formulae, which extract such features as stative or active for adjectives, or memberset relations for nouns. These are adequate because the patterns described are generally all found at or close to the beginning of tile definition text. But the most interesting patterns that identify the differentiae and tile (possibly embedded) semantic relations expressed therein rely on 2The parse trees in this paper are altered representations isomorphic to actual machine output which IBM ASD has not allowed us to reproduce. Heads of constituents are directly below their parent node and the nodename is in bold.</Paragraph> <Paragraph position="1"> SPPs are analyzed with a preposition premodifier and a nominal as the head.</Paragraph> <Paragraph position="2"> complex stnzctural iuformation, information which cannot be expressed adequately in string patterns, The following addition makes the pattern for extracting the PURPOSE relation, paratthrased in the previous section, more complete: if tile PP with &quot;for&quot; is not a post-modifier of a verb &quot;used&quot;, then a PURPOSE relation between the definiendum and the head(s) of the PP c,'m be hypothesized if the nearest noun that the PP post-modifies is the genus term.4 Consider the syntactic analysis of the relevant portion of text in the definition of &quot;laboratory&quot; (W7 n,l) shown below in Figure 5. Since PP2 and PP4 are coordinated, tile structural relation to the rest of the analysis will be tested for tile conjoined constituent, PP1. The nearest noun phrase that PP1 post-modifies is NP1, the head of which, NOUN1, is indeed the genus term (also identified automatically by structucal patterns applying to this analysis.) Thus, part of tile semmltic frame for Sense 1 of &quot;laboratory&quot; will be: from which it was derived.</Paragraph> <Paragraph position="3"> Now consider the syntactic analysis of the relevant portion of text in the definition of &quot;council&quot; (LDOCE n): &quot;a group of people appointed or elected to make laws, rules, or decisions, for a town, church, etc., or to give advice&quot;: 4Currently, for English, an abstract relation 1S-FOR ks extracted which will satisfy any searches for a PURPOSE relation.</Paragraph> <Paragraph position="4"> ACRES DE COLING-92, NANTES, 23-28 Ao~r 1992 5 4 8 PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 io,~, da~ *re Figure 6. &quot;for&quot;-PP that does not create a PURPOSE relationship.</Paragraph> <Paragraph position="5"> Tile nearest noun phra~ that PPI post-modifies is NPI, which is a coordimlted construction. None of the heads of NPI, &quot;laws&quot;, &quot;rules&quot; or &quot;decisions&quot; can he identified as the genus term, and so tile patteru does not succeed in extracting a PURPOSE relation from this definition.</Paragraph> <Paragraph position="6"> In order to write a string pattern that would correctly identify tile semantic relations above, the pattern would have to identify conjoined heads and apply some measure of distance from the genus while counting conjoined phrases as single units. In addition, string patterns would also have to skip parentheses, identify functional arguments, and abstract from the surface realizations of the pattern, e.g. pre- and post-modification (similar observations are made in Ktavans 1990). Even if the language of dictionary definitions is characterized by its regularity, variations of the defining formulae exist. These restrictions seem to be far too complex at tile string level, while writing the patteru at the level of syntactic analysis describes the dependency in an iutuitive manner, namely in terms of heads and modifiers.</Paragraph> <Paragraph position="7"> The inadequacy of string patterns is not only evident when extracting the semantic relations directly related with the definiendum, but also when extracting those relations that show further specifications. In particular, the HAS-SUBJECT and HAS-OBJECT relations cannot possibly be extracted reliahly without structural information. Wider syntactic context is also required to correctly extract the semantic features such as COLOR, SHAPE, TASTE, and SMELL not only as features of tile definiendum, but also as further specifications of the words extracted as the vahms of .semantic relations.</Paragraph> <Paragraph position="8"> The structural pattern that extracts semantic features such as COLOR and TASTE would seem to be trivial: modifying adjectives or nouns that express these properties. The attachment of these modifiers, however, can be established only ou the basis of syntactic information (and sometimes syntax is not enough). And only those modifiers should be extracted that relate to the definiendum or those that relate to some other word within the definition which stands in some semantic relation (for instance HAS-PART, MADE-O1:. and so forth) with the definienduul. In tile tatter case tbe informatiml extracted still has an indirect link with the iemma I~eing defined, but it is not expected to be interpreted as a semantic feature of lhe dcfiniendum itself.</Paragraph> <Paragraph position="9"> Consider these examples from tile Garzanti dictionary (followed hy their English glosses): acagiil: &quot;alhcro tropicale dai ti'utti saporiti.&quot; (mahogany tree: tropical tree with tasty fruits) alchechengi: &quot;pianta erbaceal con bacche di color arancio racchiuse in uu involucre membranaceo, commestibile.&quot; (winter cherry: herbaceous plant with orange berries, contained in a membranaceous coveting, edihle).</Paragraph> <Paragraph position="10"> The TASTE aml tile COl,OR features should not be extracted as seamntic features of the definicndum. In the case of &quot;acagifi,&quot; this is clear due to the lack of agreement between &quot;albero&quot; (tree) and &quot;sapotiti&quot; (tasty): tile adjective cannot modify the head noun/genus term Ixx:ause they do not agree in nmnber. &quot;Saporito&quot;, however, is the value of the semantic feature TASTE of &quot;frutto&quot; (fruit), which is in turn the value of the HAS-PART relation of the definiendum, also extracted by means of a structural pattern front the dcfinition text. The semantic frame for &quot;acagiil'&quot; is showu in Figure 7: AcrEs DE COLING-92, NANTES, 23-28 .~O1~'1' 1992 5 4 9 Plmc. OF COLING-92. NANr~;s, AUt~. 23-28, 1992 If we consider the shaictural description of the definition for &quot;alchechengi,&quot; we can see clearly that the embedding of PP2 within the syntactic structure, followed by another modifier of &quot;bacche,&quot; AJP1, makes it impossible for PP2, &quot;di color arancio,&quot; to modify Ihe head noun &quot;pianta&quot;, and so the semantic fczlture COLOR &quot;anancio&quot; is extracted for &quot;bacche&quot;, which is in a PART-OF relation with the definiendum.</Paragraph> <Paragraph position="11"> Syntactic information is not always sufficient for resolving the correct assigmnenl of semanfic fealures. Consider the DMI definition for &quot;agnolotto&quot; (a kind of ravioli): agnolottn: &quot;involucro di pasta all'uovo rolondo o rettaugolarc.&quot; (ravinli: ronnd or rectangular covering of egg pastry) The attacimlent of the adjectival phrase &quot;rotondo o rettangolare&quot; is ambiguous and cmrnot be determined on the hasis of syntactic information, but only I)ased ou semantic information; the correct analysis would read a &quot;round or rectangular covering&quot; and not &quot;a round or rectangular egg.&quot; Despite this syntactic ambiguity, the range in ambiguity for extracting semantic relatimrs and tcatures is quite reduced if we start from syntactic structures instead of from simple strings.</Paragraph> <Paragraph position="12"> 5. Why a general text parser is sufficient There are two rea~ns wily a general text parser is essential fo~ providing the syntactic analyses. First, of the four dictionaries that have been explored in this research, Garzanti and DMI (for Italian) and LDOCE and W7 (for Englisi0, only LDOCE attempts to nse a restricted vocabulary in the definition texts. Therefore, Ihe scope of the vocabulary is the same as unrestricted text. Moreover, the language used in dictionaries cannot appropriately be called a specialized language given that it does not operate in a specialized domain. Second, at tire syntactic level, the variety of couslruclions can be compared to thai of textual corpora. The regularity of the language used within dictionary definitions, lexically and syntactically constnained, lies in the flequent occurrence of lexieal and syulactic patterns to express particular conceptual categories or semantic relations. This regularity, which is crucial with respect to the extraction of semantic information, can be considered almost irrelevant from tire point of view of persing I)ecause of the variety of lexical choices and phrasal constnlctions used to express tile patterns. A parser, therefore, is faced with the ,,anne range of p\['oblems in arralyzing ordinary texts as in dictionary dcfinitions and so the use of a gencral ptapose grammar is a lundmnental choice in the definition of our research framework.</Paragraph> <Paragraph position="13"> One of the main disadvantages ascril)ed to using general tent parsers is the mobiguity still remaining at the end of the synt:~cfic analysis. It has oficn been observed that de~riptions associated with syntactically ambiguous coustfuctions ill fi'ee text can bc di~ambiguated in the context of dictionary definitions. For example, within our system the default strategy in free text is to attach a prepositional phrase to tile nearest available head and to keep track of the alternative possible attachment sites. In the context of dictionary definitions, the choice resulting from such a default strategy carl often be overridden on the basis of lexical and/or syntactic conditions which disamhiguale tile potential ambiguity; for instance, with regard to the PP attachment case, there is a class of genus terms (such as &quot;atlo,&quot; act, &quot;effetto.&quot; effect; &quot;processo,&quot; process) that, together with given structural conditions, make the attachment decision l)ossihle.</Paragraph> <Paragraph position="14"> Also, while functional assignment may be ambiguous in Italian in some cases (Chauod et al. 1991). we can assume that constructions used within dictiomuy definitions and example senlcnces are always unmarked, and consequently that the ambiguity derived from taking into account also marked orders of sentence constituents (such as Subject-Object-Verb. Object-Verb-Subject and so forth) is very unlikely to occur in the dictionary text.</Paragraph> <Paragraph position="15"> Rather than taking these observations as justification for building a dictionary specific parser, we use first a broad coverage parser, followed by a post-processor which tailors the output of the parser based oil the differences observed between dictionary text and general text. As it turns out, the size of file post-processor is negligible compared to the size of the grammar. This supl)orts our claim that the variety of syntactic constructions in dictionary text is comparable to that of textmd eorp~)ra. If dictionary text were substantially different from general text, we would have had to write more rtdes in the posl-processor and it would have to be bigger than it in fact is. Tile structural patterns for the extraction of semantic information naturally operate on the result of the post-processor (see Montemagni 1992).</Paragraph> <Paragraph position="16"> Twn kinds of refinements have been devised in order to achieve more appropriate results with respect to the I~mguage used within diclionaries: (1) rule out ambiguity in the attachment of modifiers or in the assignment of functional roles which is not applicable in the context of dictionary definitions; (2) handle parses that are incomplete due to either dictionary specific constructions not occurring in free texts, or, more generally, to gaps in tile lexical or grammatical knowledge of the system.</Paragraph> <Paragraph position="17"> While the first refinement operates on a complete analysis but aims to reduce the high degree of ambiguity typical of free text by exploiting pcculiarities of dictionary language. the second refinement concerns thc robustness of the system in the abscnce of a complete parse.</Paragraph> <Paragraph position="18"> For an example of refining the parse in order to reduce the ambiguity, consider the Garzanti definition (n,l) of &quot;comput:~ione&quot; (computation) defined ,as &quot;alto, effelto del computare&quot; (the act or result of computing). The first Acrl!s DI~ CO\[JNG-92. NANTES, 23-28 AO0r 1992 5 5 0 PROC. OF COLING-92, NAICrES, AUG. 23-28, 1992 stnlctural description below shows the NP parse for geueml text. This default analysis shows PP1 &quot;del computare&quot; in be attached to the closest availahle head, NOUN2 &quot;effetto&quot;, while the alternative attachment site is malked with a question nmrk. The second parse below shows the resolution of the PP attachment ambiguity; PPI now modifies tile coordinated nouns NOUNI ~md NOUN2.</Paragraph> <Paragraph position="19"> This refinement is made when a prepositional phrase or an infinitival clause post-modifies e(~)rdinated bead nouns that are the top nodes of the syntactic analysis. This is the typica |paltenl of the definitions of deverbal nouns; the PP indicates which verb the definiendum is derived from. The lexical and synlactic couditions which make tim disamhigualion possible ;tre defined in the l~)sVprocessor to the general text analysis.</Paragraph> <Paragraph position="20"> Tbe solution to a robust phrasal analysis while parsing dictionary text with a general grammar cau he secn and faced from two different perspectives. The first perspective is dictionary specific and de,'ds with incomplete pauses due to input which would be considered ungrammatical outsidc of the context of dictionary definitions. The second perspective copes with incomplete knowledge of language use by exploiting the general technique of fitted parsing provided by the system fi)r handling ill-formed inpnt (Jensen et al. 19831.</Paragraph> <Paragraph position="21"> Dictionary definitions are quite often fl)rnmlated as condensed fragments of real texts, with elidcd elements which make the definition syntactically ill-formed and interpretable only by reference to a wider context. This is tim case with noun definitions consisting of a noun phrase pre-modified by a prepositional phrxse, where the latter specifies the usage domain of the word sense expressed by the former. The general grammar is unable to produce an NP node covering the whole input string given that the sequence PP-NP does not freely occur within ordinary texts.</Paragraph> <Paragraph position="22"> It is the refinement stage that should reshape the analysis and restore it ,as regular input on the basis of Slrecialized diclionat y use. The aualysis below of the Garzxudi definition 111,1,2) &quot;nettare&quot; (nectar), defined as &quot;nella nlitologia classica, la bewm&t degli dei&quot; (in classical mythoh)gy, the drink of gods) exenrplifies this kind of le fiuenlen\[.</Paragraph> <Paragraph position="24"> Figtue 10. ReI\]nentenl of fitted parse into NP.</Paragraph> <Paragraph position="25"> The filst of tile two llarses alnJve has I~..cn generated by the general Erauunar; the XXXX1 label at the top node shows that the i)alsc is incomple|e, q'he second one has been rebuilt (hit ing the refincnlem stage: the XXXX1 has IKven replaced hy the l)fol~r lahcl NPI. In this case, knowledge ol dicti(nlary peculiarities resolves the initial partial parse and converts it into a complete and succc.ssful analysis.</Paragraph> <Paragraph position="26"> Not all iucolnplete parses can l)~ so easily restructured.</Paragraph> <Paragraph position="27"> Some are due to gaps in the system with respect to lexical as well as phlase constrnction knowledge. Those cases are handled hy lacilifies in tbe fitting pa~cedure, provided by the system to cope with umeslricted input. Wilen the g~tllnUar is unable to produce at coulplete ~malysis, then a reasonable atlPt+oximate but incomplete structure is assigned to the input.</Paragraph> <Paragraph position="28"> Such a lOU~,.h parse Call still be used as input Ibr fltrther i)rocessi,;,~ ~;t~tges and h)i' the extraction procedure itself. By allowing stn~ctural l)atterus in apply to incomplete parses as well, the auto,italic extraction (If semantic iuh)rmatiou is not threatened. There is, however, a difference in the extraction pa)cednre applied to complete (i.e. computed by tbe geuel~d grammar or restored during the posl-processing stage) and incomplete analyses. While slructm-al patlenis are used 1o extract semantic infonlmtion frmn definitions and example ~ntences successfully parsed, partial structural patterns and string palterm; are combiueu.l when handling incomplete pa~ses. By differentiating the ACI'I!S DE COI~/qG-92, NAIVrEs, 23-28 Anus 1992 5 5 i lqtC/)C/:. ()l: COLIN(;-92, NAN-rEs, Au(t. 23-28, 1992 extraction procedure for the two kinds of results, the procedure becomes robust and overcomes the variability of parsing performauces.</Paragraph> <Paragraph position="29"> Finally, a brief account of the parsing performance of the Italian grammar for a corpus of 1000 definitions. The general Italian grammar provided complete parses for about 65-70% oflhese definitions. An improvement comprising 10-15% of the total was achieved during the refinement stage. For the unresolved incomplete parses, approximately 15-20%, a different extraction procedure, based on a combination of partial structural patterns and string patterns as described above, has been hypolhesized. Even if this procedure is at an early development stage, it is possible to evaluate the first results. Because of the robust strategy, the extraction procedure can be applied to lhe entire corpus of definitions, without the worry Ihat incomplete parses would affect the extraction of semantic information. Some information is extracted in any ease; in the worst case the information is not very deep or detailed (at least the genus term is extracted). The results can be differentiated by degree of detail, but the extraction procedure never fails to produce some results.</Paragraph> </Section> class="xml-element"></Paper>