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<?xml version="1.0" standalone="yes"?> <Paper uid="C86-1031"> <Title>Parsing in Parallel</Title> <Section position="3" start_page="0" end_page="140" type="metho"> <SectionTitle> 2. Semantic Definite Clause Grammars </SectionTitle> <Paragraph position="0"> The SDCG is em'rently implemented on a single processor machine where it is the parser for the XTRA (English Chinese Sentence Translator) machine translation system \[Huang 85\]. The XTRA is a prototype system now running nnd&&quot; a C-prolog interpreter and fias a wide coverage of English phenomena, even though its vocabulary is rather small (1000 entries). The SDCG uses the semantics of words and phrases to restrict the number of syntactic pm~es of a sentence to those which arc semantically compatible. A simplified vemion of the SDCG used in the XTRA system is as follows:</Paragraph> <Paragraph position="2"> The graminar says that an input string is a sentence with the structure s(Sub3: Np, vp-(vYVerb sense) Obj Np)).</Paragraph> <Paragraph position="3"> if it is composed of Su(~j NP which is-a noun{ phrase, followed by Verb (a ve,'b) whose one sense Verb sense is semantically compat:ble with Subj Np, followed by Obj NP (a noun phrase) which is semanti- cally compatible-with Verb_sense. The sub-grammar for pa,'sing a noun phrase is as follows: (2) noun phrase\[np(det(DeQ, adj(Adj sense), n(Nonn ~ense)))--> determiner(Det), null adjective(Adjective), noun(Noun), adj noun match(Adjective, Noun, - -Adj se'nse, Nounsense).</Paragraph> <Paragraph position="4"> The last predicate ill the noun phl'~Lse subgrammar, 'adj_jiounAI:ateh', tries to match Adjective and Noun to find a compatible pail&quot; of senses for tile given Adjective and Noun to be eombined. The predicates'sub\] verb. match' and 'verb object mateh'inihe sentence gralnmar accomplish shn-ilar ta~k. All those matches are based on the system of seleetional restrictions proposed by \[Katz & Fodor 63\] and their eodings are omitted here to save spaee, l,ater we will see how they function.</Paragraph> <Paragraph position="5"> There is a syntactic lexicon ia the SDCG of the following form: determiner(the).</Paragraph> <Paragraph position="6"> noun(coach,\[eoacht,eoaeh2\]).</Paragraph> <Paragraph position="7"> noun(star,\[starl,star2 D.</Paragraph> <Paragraph position="8"> adjective(tough,\[tough1 ,tough2,toug h3,tough4\]). verb(marry,\[mar,'yl ,inarry2\]).</Paragraph> <Paragraph position="9"> For instance, the syntactic entry for &quot;coach&quot; is a noun having two senses, labeled &quot;eoacbl&quot; and &quot;coach2&quot;.</Paragraph> <Paragraph position="10"> For each word sense, a semantie interpretation is given in the semantic dictionary: sem(coaehl,\[head(thing)\])*. (eg. 'a passenger in &quot;IIe mm'ried money. ) For example, &quot;coach1 labels the sense of &quot;coach&quot; whereby it refms to a &quot;tlfing . in pa~sing (3), (3) The I, ough coach married a star*.</Paragraph> <Paragraph position="11"> according to the grammar in (1) the system starts with the predicate 'nonnA)lu'~me', which is presented in (2). After it instantiates tile variables Det, Adjective and Noun instantiated to the' tough&quot; and coach&quot; it attempts to apply the predleate 'adj noun match', whose task it is to find tile first pair of senses for the words &quot;tough&quot; and &quot;eoach&quot;, respectively, which are compatible with each other aeeording to our seleetional restrietions. Here I, he first pair found would be 'loath1 + coach1', beeause the semantic category of &quot;coach1&quot; ('thing') fibs into bile 'poss(thing)' slot of the word sense %oughl&quot; (meaning that his adjectival sense is for modifying sometlfing whose semant, ic category is 'thing').</Paragraph> <Paragraph position="12"> Now tile parser is at tile predicate 'is verb' where it finds the verb &quot;marry&quot;. It, t, hen tr:es to mateh Subj ,Np (%otigh\] -I- coachl') witt: a some sense of the &quot;mam'y&quot; but fails because both &quot;marryl&quot; and &quot;marry2&quot; prefer the subject to be of the semantic category 'man', which &quot;coachl&quot; cannot satisfy. The system b~ektracks, trying 'adj noun match' again and producing the next matclfing-pah&quot; of senses for &quot;the tough coach&quot; ('tough2 -I- coach2'). YVhei} 'subj verb mateh is tried again and it selects 'marry1 as tI:~ appTopriate verb sense. The parser proceeds to analyse the rest of tim sentence, employing &quot;noun_0hrase&quot; to find the object noun phr~e sense * The semantic primitives sneh a.s 'thing', 'man', ere, are based ell the primitive set suggested in \[Wilks 75\]. * Modified version of the &quot;semantie garden path&quot; sentence by \[Chm'niak 83\] (&quot;The astronomer married the star,&quot;) and &quot;verb_obj_match&quot; to see whether this noun phrase sense lits the partieular verb sense. 'S~arl' (a eelest:al object) is thus tried and rejected, and 'Stal'2' (a celebrity) is accepted ('marry1' requires the object t'o be of the semantic category man ). A plausible zeadlng of the sentence is thus gained (' &quot;File strict ~ralner married a celebril, y.&quot;) It is clear from t, he above description that in the SI)CG syntax and semantics closely interact: syntax semantics -. syntax, ere. One class of predicate waits for the other to make a decision, then makes its own decision. \]low much baektraeking must be done is unpredictable; the pm'se might only be completed after several routes have been tried and rejeel, ed.</Paragraph> </Section> <Section position="4" start_page="140" end_page="141" type="metho"> <SectionTitle> 3. Parallel Parsing </SectionTitle> <Paragraph position="0"> The model consists of six processes which con> munlcate to produce all the semantically compatible parses of a given sentence. Each process will be hnplemented as a tree of processors. The root node of t, he tree eonl~ains a queue of requests and allocates processors to the elements of l, he qneue as they become awdlable. For the pnrpose of this model it is sulIicent to note f, hat each process itself has {,he capability of processing several requests in parallel. We identify below each of the processes and describe the communication between them. 1) Sentenee maste,&quot; - Controlling process which operates as a modified top down syntactic processor (modified in the sense thaC infer\[nation fi'om el, her processes influences its decisions). 2) Noun-phrase m~ster (NP-master)- Given an arbitrary string, it identifies syhtactieally all possible initial noun phr~mes in the string. Ttn'ough eommuilication with the AN-master, it del, ernfines which of I, hese are senmntieally acceptable.</Paragraph> <Paragraph position="1"> 3) Semantic dictionary lna.ster - Contains the semantic dictionary and provides appropriate entries for the current input sentence to the other semantic processes.</Paragraph> <Paragraph position="2"> 4) Adjective-Noun master (AN-master) - null Given an adjective and a noun, Iiads all possible pairs (adjective word sense, neat\] we &quot;d sense) thai are compatible.</Paragraph> <Paragraph position="3"> 5) Subject-Verb master (SV-master)- Given a word sense for a nou6 and a verb, finds all possible word senses for the verb that are compatible.</Paragraph> <Paragraph position="4"> 6) Verb-Objeel, master (VO-lm~ster) - Given a word sense of a verb and a word sense of a noun, determines whether or not that verb sense-object noun sense pair \]s compatible.</Paragraph> <Paragraph position="5"> The following diagram illustrates tile processes and the eommnnieation between them.</Paragraph> <Paragraph position="6"> r .......... input ........... : Input is read simultaneously by the semantic dic.~ tionary, and the sentence master. The sentence master contains the s,vutaetic dictionary and tlegins a top-down parse of tim sentence guided by the definite clause grammar. Wimnever a noun piu'ase is searched for, the noun phrase master is invoked to produce all possible initial noun phrases in the remainder (tllc unparsed portion) of the input string. After the main verb of any clause imps been identified by tim sentence master, the SV-master is invoked to produce all possible verb senses which are meaningfulat this point in the parse. Ill tim case that a transitive verb is found and a possible word sense fox&quot; the object noun is determined, the VO-master is consulted as to wheti~er or not the given verb word sense and object noun word sense are acceptable as a verb-object pail'.</Paragraph> <Paragraph position="7"> In communicating witl~ the NP-master or SVmaster, several possibilities may be returned to the sentence master, and the parse is continued fox&quot; each of these possibilities in parallel.</Paragraph> <Paragraph position="8"> Tile NP-master, which is also a syntactic process, finds all possible initial noun phrases which are meaningful by using its own syntactic information (in a top down manner)and by communicating with tile AN-master for semantic information. This communication is similar to that of the sentence master witll tile SVmaster. After determining an adjective which is followed by a noun, ti~e NP-master invokes the AN-master to tind all meaningful adjective-noun word sense pairs. Multiple adjectives which modify a noun are considered in parallel by the AN-master, which in ti~is ease, returns pairs which consist of a list of adjective word senses and a noun word sense. Whenever the NP-master reeives a pair from the AN-master, it continues any work that it might lmve (such as finding prepositional pin'ascs which modify tile noun, e.g. 'the big boy in the park'). If several pairs are returned by the AN-master, the remainder of the parse is handled by the NP-master and is done in parallel when possible. The sentence master produces all the parses of the sentence that have not been blocked. A parse may be blocked rot any one of the following three reasons: We use the example in Section 2 (&quot;The tough coach married a star.&quot;) to illustrate the above communication of processes and to exhibit a path whiei~ is blocked.</Paragraph> <Paragraph position="9"> For shnplieity, we write the SDCG used previously, without the arguments for the predicates involved. We also add an additional rule for nounphrase and another entry in the semantic dictionary for the noun sense of 'tough', tougi13 (as in &quot;the tough never suffer&quot;), to make the example interesting. sentence --:> nounphrase, verb, subject verb match, noun phrase,verb_obj eet_mat oh.</Paragraph> <Paragraph position="10"> nounA)ln'ase--:> determiner, adjective, noun, adj noun match.</Paragraph> <Paragraph position="11"> noun_phrase -- > determiner, noun.</Paragraph> <Paragraph position="12"> determiner--> \[the\].</Paragraph> <Paragraph position="13"> determiner -- > \[\].</Paragraph> <Paragraph position="14"> The sentence master receives the input and in this ease, immediately passes it to the NP-master and waits. The NP-master finds &quot;The tough&quot; and &quot;The tough coach&quot; as possible initial noun phrases in the string it was given. &quot;The tough&quot; (tough_3) is returned immediately to the sentence master who begins searching fox&quot; u verb. Sinlultaneously, ti~e NP master sends the adjective noun pair, (tough, coach) to the AN-master. The AN-master returns (\[oughl, coachl) (&quot;rugged vehicle&quot;) and (tough2, coacil2) (&quot;strict trainer&quot;). Note that these are the same possibilities considered by back-tracking in the example in Section 2. The NP-master returns these to the sentence m~ster, who initiates the continuation of the parse fox&quot; each of timse possibilities. The sentence master, in tim interim, found a verb (coach) for its frst noun-phrase (the tou{~!13) and request a subject-verb match from the Sv-master.</Paragraph> <Paragraph position="15"> The SV-master returns coacil3 (the verb sense of coach) and the sentence master continues with the remainder of the input string &quot;married a star&quot;. Here, a noun A0hrase is needed, and so once again the NPm'mter is invoked, and asked to find an initial noun phrase in the string. Since no noun phrase is found, this path is blocked. The path containing (tougM,coachl) will be blocked exactly as the description in Section 2. The path containing (tough2,eoaeb2) will succeed and produce the correct parse fox&quot; the sentence. null We now consider the function of the Semantic Dictionary master. While the sentence master is receiving its .input and begins the processing described above, the semantic dictionary master simultaneously finds all possible word senses for cacti input word. The semantic dictionary contains an entry mr each sense of a word. The structure of each entry reveals its syntactic category. Word senses corresponding to nouns contain only the semantic class to whici~ the word sense belongs. For example, the semantic dictionary entry fox&quot; tile noun &quot;name '~ (as in the gh'l's name) is given by: sere(name1, \[>ad(sign)l ).</Paragraph> <Paragraph position="16"> Adjective word senses contain the semantic class of the noun that it prefers to modify. The adjective &quot;specific&quot; has the following entry: sere(specific1, \[poss(sign)\]).</Paragraph> <Paragraph position="17"> Word senses corresponding to verbs m'e described witi~ a structm'e which contains the class of the subject that is prefered by this verb, the class of' the object prefered, and the semantic class of tim verb itself. The verb &quot;name&quot; (&quot;to name a dog&quot;) is represented as: sere(name2, \[subj(man), obj(man), head(make)l ).</Paragraph> <Paragraph position="18"> After finding all possible word senses for words in the input sentence, the semantic dictionary master sends these dictionary entries to the appropriate semantic processes. Verb entries are sent to the SV- and VO-masters, adjectives are sent to the AN-master, and nouns arc sent to all three. These three process masters then contain a &quot;cache&quot; of the semantic dictionary entries relevant to the parsing of the present input sentence. The purpose of the &quot;cache&quot; is so that the semantic dictionary entry fox&quot; any input word can be quickly found by the processes which use these entries.</Paragraph> </Section> <Section position="5" start_page="141" end_page="144" type="metho"> <SectionTitle> 4. The Design of the system </SectionTitle> <Paragraph position="0"> >k We describe the design of the implementation of the parallel parsing model. Each of the six processes consists of a tree of processors. We label the root of each process tree with the name of the process that it represents. The design of the semantic processors and the noun-phrase master is independant of tim implementation of the SDCG which is used. The design of the sentence master, however, is heavily dependant on the formal grammar used for the SDCG implementation as the parser fox' XTRA. The two syntactic processes above, the NP-master and the sentence master, have a significantly more complex design than those of the semantic processes so that different possible syntactic alternatives may be considered in parallel. *Although tile actual implementation has not begun, we hope to do so by summer 1986 when the Hypercube multiproeessor will have been ready for use.</Paragraph> <Paragraph position="1"> 4.1. The sentence master The desi~;n of tile sentence master is based on the following production rules of the SDCG: sentence--> sentence 1)ody.</Paragraph> <Paragraph position="2"> sentence --> sentence head, scntence_flody.</Paragraph> <Paragraph position="3"> Intuitively, we (:an consider the sentence head to be wbateve,' appears before the scntenee snbje~ 0t can be an empty string), and the sentence body Lo be the remainder of the sentence.</Paragraph> <Paragraph position="4"> The sentence master, as illustrated below, can be thought of as the root of a tree which h~s two children whieh we will refer to as the sentence monitors: the sentence head monitor (SH-monitor) and the sentence body monitor (SB.-monitor). Each sentence nlonitor is tile root of a sub\]red of ehild processors (SiMlandlers and SB-haadlers) and acts as ,'t monitor foi' these child p,'ocessors. We later describe the sentenec handlers in more detail.</Paragraph> <Paragraph position="5"> Sentence master</Paragraph> <Paragraph position="7"> The sentence minster is the process which determines whether or \]lot a st,'ing is a sentence. Any input to the sentence master is immediately given I,o both the SliI-monitor and the SB-nlonlto,&quot; to examine in parMlel the possibilities that the sentence does trod does not have a sentence head. The SH-monitors and the SB\]nonitors each put incoming requests from the sentenec In~ter in a queue and allocate the first available child processor t,) begin its work. In the ease of a SHhandler, this work is to identify a possible sentence bead, and in the case of an SB-handler, it is to see if the input string is a sentence body. The SII-handlers and SB-handlers monitor child processes which operate in parMlel.</Paragraph> <Paragraph position="8"> in the egse that a sentence head is found by one of the SII-handlers, the. result is retnrned to tile sentence master via the SH-monitor. The remainder of the input is then given to the SB-monitor which allocates a fl'ee SB-handle,&quot; to continue the parse of the remainder of the sentence. For example, consider the sentence: (4) Writing to ,Iohn was dill\]cult.</Paragraph> <Paragraph position="9"> The sentence master gives the sentence to both the SII-handler and tile SB-monitor which in turn give it to one of their children, say SII-handlerl and SB-handlerl.</Paragraph> <Paragraph position="10"> Since the grammar for tile SDCG indicates that an lag--clause is a possible sentence head, SH-handlerl will identify &quot;writing to .lohn&quot; as a candidate sentence head. \[he ,emamder of the sentence &quot;was difficult&quot; is given to a new SB-handler, say SB-handler2 via the SH-monitor and the SBqnonitor, to see if this is a possible sentence body. SBdeghandler2 fails and notifies SHhandler1 (via tile SIt- and SB-monitors). Sit-handle,'1 and SB-handlel'2 become available for' other processing and SB-handlerl succeeds in showing that &quot;WrilAng to ,John was dilrieult&quot; is a legal sentence body.</Paragraph> <Paragraph position="11"> The S\]-l-handlers and tile SB-handlers are arrays of ~ roeessors which implement the or-parallelism of Prolog )r the predicates sentence_head and sentencebody respectively. Below is a simplified version of the grammar rules used in the SDCG for sentence head.</Paragraph> <Paragraph position="12"> sentence head --> ing-elause.</Paragraph> <Paragraph position="13"> sentence head--> prepositionalphrase.</Paragraph> <Paragraph position="14"> sentence-head --> adverbial phrase.</Paragraph> <Paragraph position="15"> Based on these rules, each Sil-handle,&quot; monitors three child processors: SH-handler pl epomtronal plu ase m~ clause par enthetidal_plu ase Tim SB-handlers monitor five p,'ocessors which are again based on the SDCG. The function of these five child processes will vary depending on the type of the input sentence (declarative, interrogative or imperative). We give he,'e a simplified version of the senl;enee_body productions in the SDCG for a deelara.tire sentence.</Paragraph> <Paragraph position="16"> sentence_body --:> subject np, vpl.</Paragraph> <Paragraph position="17"> sentence body --> subject np, vp2.</Paragraph> <Paragraph position="18"> sentencebody -- > inverted_sentence.</Paragraph> <Paragraph position="19"> subject np--> noun I)hrase.</Paragraph> <Paragraph position="20"> subject_np -.- > ing_.elause.</Paragraph> <Paragraph position="21"> Here vpl represents a complete verb phrase, like that in the sentence (5) John didn't go to the park yesterday.</Paragraph> <Paragraph position="22"> And vp2 ,'epresents an elliptical verb phra.se, like &quot;didn't&quot; in (6) No, John didn't.</Paragraph> <Paragraph position="23"> An illust,'ation of the SB-handlers in this case is given below.</Paragraph> <Paragraph position="24"> Sl:l-handler noun phrase mg clause nounA)hrase lag_clause inverted~sentenee vpl vpl vp2 vp2 in Section 3 we indicated that the sentence master communieaters with the NP-master. Actually, each of the child processors of the sentence handlers sends a message to the NP-master, via the sentence master, whenever tile DCG dictates that a noun phrase should be found next in tile input string. The NP-master returns all semantically compatible noun phrases.</Paragraph> <Paragraph position="25"> Where there is more than one acceptable noun phrase, a mess.age m sent to tile requesting sentence handler who allocates one possible noun phrase to the waiting child processor and distributes the others to available child processors. Each child process of tile sentence handlers communicates with the NP- , SV-, and VOma:sters via the sentence master.</Paragraph> <Paragraph position="26"> It is ~)ossible that one of the child processors of the sentence handlers needs to know whether or not some subclause is itself a sentence. For example, if one of the paths of, say, SB-handlerl does a reeursivc call to check whether or not the next phrase is a sentence (as in a parenthetical expression or a conjunctive sentence), a message is sent to the sentence master to take care of this request. Tile requesting processor waits.</Paragraph> <Paragraph position="27"> Should each of the sentence handlers have a waiting child processor and the sentence master a request, we invoke a special processor, called the black-sheep processor, to grant the request, so that the requesting processes may continue. The black-sheep processor, functions precisely as the current single processor implementation of the SDCG and will only be used to avoid deadlock*.</Paragraph> <Paragraph position="28"> 4.2. The Noun-phrase master Since noun phrases are the major building block of many substructures of a sentence, and since ambiguity often arises through determination of different noun phrases (eg. &quot;The tough coach the young&quot; and &quot;The prime number consecutively&quot;), the identification of noun phrases is an important place tbr parallelism in the parser. The NP-master can be thought of as the root of a tree of processors. It functions similarly to the sentence master. The noun-phrase master contains a queue of noun-phrase requests and allocates them to available noun-phrase handlers.</Paragraph> <Paragraph position="29"> Noun-~ Each noun-phrase handler monitors three child processors. The child processors try to parse the next input phrase as a noun phrase with no adjectives, one adjective, and two or more adjectives respectively.</Paragraph> <Paragraph position="30"> For example, in parsing the phrase &quot;the tough coach,&quot; two of the child processors would succeed (no adjectives and one adjective), these results are reported to the parent noun-~phrase handler, and then sent to the sentenee master via the NP-master. At this point, the waiting sentence processor (child of either one of the SB-handlers or one of the SH-hundlers) continues with one of the possibilities and an available sibling processor is allocated by the sentence handler to continue the parse of the sentence using the other possible noun phrase.</Paragraph> <Paragraph position="31"> In the ease of a truly ambiguous sentence, all legal parses are eventually produced. The above example would produce two parses in the case of &quot;The tough coach married people&quot;?, but not in the case of &quot;The tough coach the young.</Paragraph> <Paragraph position="32"> Each of the child processes of the noun-phrase handler communicates with the AN-master via the noun-phrase handler.</Paragraph> <Section position="1" start_page="143" end_page="144" type="sub_section"> <SectionTitle> 4.3. The Semantic Processors </SectionTitle> <Paragraph position="0"> The semantic dictionary master and the AN-, SVand VO-master processor trees have a much simpler structure in that they have only two levels. The root node is the master; children of the root are handlcm.</Paragraph> <Paragraph position="1"> The Semantic dictionary entries are divided among the semantic dictionary handlers. The Semantic dictionary master reads the input and passes the relevant semantic entries, which it obtains from its child processors, to the AN-, SV-, and VO-masters as described in Section 3.</Paragraph> <Paragraph position="2"> The AN-master receives input which is in general a list of adjectives and u noun, from the noun-phrase handlers. It forms all possible pairs (adjective word sense, noun word sense) and allocates child processors to determine whether or not there is a semantic match. The pail'S consisting of a list of adjective word senses, and a noun word sense which matches each of the adjective word senses in the list, are returned to the NP-master.</Paragraph> <Paragraph position="3"> The SV-master and the VO-master receive input directly from the sentence processors. The input and output of these processes is exactly as deseribedin Section 3. In both cases, the semantically compatible word sense pairs are determined in parallel.</Paragraph> </Section> </Section> <Section position="6" start_page="144" end_page="144" type="metho"> <SectionTitle> 5. Future work </SectionTitle> <Paragraph position="0"> The Computing Research Laboratory (CRL) has the use of Longman's LDOCE English dictionary, which is realistic in size, prov\]des comprehensive syntactic information and also has its semantic entries both syntactically and semantically restricted, and limited to a 2000 word vocabulary. We plan to implement the Semantic Dictionary master by providing each of the semantic dictionary handlers with a portion of LDOCE.</Paragraph> <Paragraph position="1"> After the initial implementation of the designed parallel parser, we would like to see how W\]lksian Preference Semantics \[Wilks 75, Wilks et al 85\] can be realized in our parser in the sense that one or more readings (in the case of genuine ambiguity) can be selected by weighting the competing interpretations.</Paragraph> <Paragraph position="2"> We are also investigating a parallel parsing model which is driven by semantics, rather than syntax. We have in mind that the role of the sentence master in this case is purely semantic and that syntax is used only to help the segmentation of the input string.</Paragraph> <Paragraph position="3"> Comparison of the two systems would be of great interest to us. Eventually, we also want to consider the incorporation of pragmatlcs into the system.</Paragraph> </Section> class="xml-element"></Paper>