PARSING HEAD-DRIVEN PHRASE STRUCTURE GRAMMAR
Derek Proudlan and Carl Pollard Hewlett-Packard Laboratories 1501 Page Mill Road Palo Alto, CA. 94303, USA
Abstract More precisely, the subcategorization of a head is The Head-driven Phrase Structure Grammar project encoded as the value of a stack-valued feature called (HPSG) is an English language database query system ~SUBCAT". For example, the SUBCAT value of the under development at Hewlett-Packard Laboratories. verb persuade is the sequence of three categories IVP, Unlike other product-oriented efforts in the natural lan- NP, NP I, corresponding to the grammatical relations guage understanding field, the HPSG system was de- (GR's): controlled complement, direct object, and sub- signed and implemented by linguists on the basis of ject respectively. We are adopting a modified version of recent theoretical developments. But, unlike other im- Dowty's [19821 terminology for GR's, where subject "LS plementations of linguistic theories, this system is not a last, direct object second-to-last, etc. For semantic rea- toy, as it deals with a variety of practical problems not sons we call the GR following a controlled complement covered in the theoretical literature. We believe that the controller. this makes the HPSG system ,nique in its combination One of the key differences between HPSG and its of linguistic theory and practical application. predecesor GPSG is the massive relocation of linguistic The HPSG system differs from its predecessor information from phrase structure rules into the lexi- GPSG, reported on at the 1982 ACL meeting (Gawron con [5]. This wholesale lexicalization of linguistic infor- et al. 119821), in four significant respects: syntax, lexi- mation in HPSG results in a drastic reduction in the cal representation, parsing, and semantics. The paper number of phrase structure rules. Since rules no longer focuses on parsing issues, but also gives a synopsis of handle subcategorization, their sole remaining function the underlying syntactic formalism. is to encode a small number of language-specific prin- 1 Syntax ciples for projecting from [exical entries h, surface con- HPSG is a lexically based theory of phrase struc- stituent order. ture, so called because of the central role played by The schematic nature of the grammar rules allows grammlttical heads and their associated complements.' the system to parse a large fragment of English with Roughly speaking, heads are linguistic forms (words only a small number of rules (the system currently uses and phrases) tl,at exert syntactic and semantic restric- sixteen), since each r1,le can be used in many different tions on the phrases, called complements, that charac- situations. The constituents of each rule are sparsely teristically combine with them to form larger phrases. annotated with features, but are fleshed out when taken Verbs are the heads of verb phrm~es (apd sentences), together with constituents looked for and constituents nouns are the heads of noun phra~es, and so forth. found. As in most current syntactic theories, categories For example the sentence The manager works can are represented as complexes of feature specifications. be parsed using the single rule RI below. The rule But the [IPSG treatment of lcxical subcategorization is applied to build the noun phrase The manager by obviates the need in the theory of categories for the no- identifying the head H with the [exical element man- tion of bar-level (in the sense of X-bar theory, prevalent aqer and tile complement CI with the lexical element in much current linguistic research}. [n addition, the the. The entire sentence is built by ideutifying the H augmentation of the system of categories with stack- with works and the C1 with the noun phrase described valued features - features whose values ~re sequences above. Thus the single rule RI functions as both the S of categories - unilies the theory of lexical subcatego- -* NP VP, and NP ~ Det N rules of familiar context riz~tion with the theory of bi,~ding phenomena. By fRe grammars. binding pimnomena we meaa essentially noJL-clause- bounded delmndencies, ,'such a.~ th~rse involving dislo- cated constituents, relative ~Lnd interrogative pronouns, R1. x -> ci hi(CONTROL INTRANS)] a* and reflexive and reciprocal pronouns [12 I. Figure I. A Grammar Rule. * iIPSG ul a relinwlJ~i¢ ~ld ¢zt.,~nsioll ,,f th~ clu~dy rel~tteu Gt~lmr~dilmd Ph¢.'tme Structulm Gran|n|ar lTI. The detaaJs uf lily tllt~J/-y of HPSG ar~ Nt forth in Ii|[.
167 ]Feature Passing power of 2 in this range and then adding up the indices The theory of HPSG embodies a number of sub- so derived for each disjunction of values encountered. stantive hypotheses about universal granunatical prin- Unification in such cases is thus reduced to the "logical ciples, Such principles as the Head Feature Princi- and" of the integers in each cell of the vector represent- ple, the Binding Inheritance Principle, and the Con- ing the feature matrix. In this way unification of these trol Agreement Principle, require that certain syntac- flat structures can be done in constant time, and since tic features specified on daughters in syntactic trees are =logical and" is generally a single machine instruction inherited by the mothers. Highly abstract phrase struc- the overhead is very low. ture rules thus give rise to fully specified grammatical structures in a recursive process driven by syntactic in- N V A P formation encoded on lexical heads. Thus HPSG, un- like similar ~unification-based" syntactic theories, em- [ t [ 0 [ t [ 0 [ = tO = (MAJ N A) bodies a strong hypothesis about the flow of relevant information in the derivation of complex structures. It It 10 io l= t2= (MAJ gV) Unification Another important difference between HPSG and Ilmllmllmmlllllll~l Un£fication other unification based syntactic theories concerns the form of the expressions which are actually unified. I I I 0 I 0 I 0 I = 8 = (MAJ H) In HPSG, the structures which get unified are (with limited exceptions to be discussed below) not general graph structures as in Lexical Functional Qrammar [1 I, Figure 4: Closeup of the MAJ feature. or Functional Unification Granmaar IlOI, but rather fiat atomic valued feature matrices, such as those ~hown There are, however, certain cases when the values below. of features are not atomic, but are instead themselves feature matrices. The unification of such structures [(CONTROL 0 INTRANS) (MAJ N A) could, in theory, involve arbitrary recursion on the gen- (AGR 3RDSG) (PRD MINUS) (TOP MINUS)] eral unification algorithm, and it would seem that we had not progressed very far from the problem of uni- [(CONTROL O) (MAJ H V) (INV PLUS)] fying general graph structures. Happily, the features for which this property of embedding holds, constitute Figure 2. Two feature matrices. a small finite set (basically tlte so called "binding fea- tures"). Thus we are able to segregate such features In the implementation of [[PSG we have been able from the rest, and recurse only when such a "category to use this restrictiou on the form of feature tnatrices valued ~ feature is present. [n practice, therefore, the to good advantage. Since for any given version of the time performance of the general uailication algorithm system the range of atomic features and feature values is very good, essentially the sanze a.s that of the lint is fixed, we are able to represent fiat feature matrices, structure unification algorithm described above. such as the ores above, as vectors of intcKers, where each cell in the vector represents a feature, and ~he in- 2 Parsing teger in each cell represents a disjunctioa of tile possible As in the earlier GPSG system, the primary job values for that feature. of the parser in the HPSG system is to produce a se- mantics for the input sentence. This is done composi- tionally as the phrase structure is built, and uses only CON MAJ AGR PRD INV TOP ... locally available information. Thus every constituent ...... which is built syntactically has a corresponding seman- i tTt toi2 It 13 I t I tics built for it at the same time, using only information available in the phrasal subtree which it immediately dominates. This locality constraint in computing the I t[ t21 7 13 I t I 3 I semantics for constituents is an essential characteristic of HPSG. For a more complete description of the se- mantic treatment used in the HPSG system see Creary Figure 3: Two ~ransduced feature matrices. and Pollard [2].
Head-driven Active Chart Parser For example, if the possible values of the MAJ fea- A crucial dilference between the HPSG system and ture are N, V, A, and P then we can uuiquely represent its predecessor GPSG is the importance placed on the any combination of these features with an integer in head constituent in HPSG. [n HPSG it is the head con- the raalge 0..15. This is accomplished simply by a~ign- stituent of a rule which carries the subcategorization ing each possible value an index which is an integral information needed to build the other constituents of
168 the rule. Thus parsing proceeds head first through the propagated to the other elements of the edge, thereby phrase structure of a sentence, rather than left to right restricting the types of constituents with which they through the sentence string. can be satisfied. Writing a constituent marker in upper The parser itself is a variation of an active chart case indicates that an inactive edge has been found parser [4,9,8,13], modified to permit the construction of to instantiate it, while a lower case (not yet found) constituents head first, instead of in left-to-right order. constituent in bold face indicates that this is the next In order to successfully parse "head first", an edge* constituent which will try to be instantiated. must be augmented to include information about its span (i.e. its position in the string). This is necessary El. V<3.3> because heaA can appear as a middle constituent of RI. x-> ci h a* a rule with other constituents (e.g. complements or g2. s<3.3> -> np VP a* adjuncts) on either side. Thus it is not possible to record all the requisite boundary information simply E3. NP
169 ("binding feature"). In particular, unbound anaphors One type of heuristic involves additional syntactic axe kept track of by two binding features, REFL (for information which can be attached to rules to deter- reflexive pronouns) and BPRO ['or personal pronouns mine their likelihood. Such a heuristic is based on the available to serve as bound anaphors. According to currently intended use for the rule to which it is at- the Binding Inheritance Principle, all categories on tached, and on the edges already available in the chart. binding-feature stacks which do not get bound under a An example of this type of heuristic is sketched below. particular node are inherited onto that node. Just how binding is effected depends on the type of dependency. RI. x -> cl h a* In the case of bound anaphora, this is accomplished by merging the relevant agreement information (stored Heuristic-l: Are the features of cl +QUE? in the REFL or BPRO stack of the constituent contain- ing the anaphor) with one of the later GR's subcatego- Figure 6: A rule with an attached heuristic. rized for by the head which governs that constituent. This has the effect of forcing the node that ultimately unifies with that GR (if any) to be the sought-after Heuristic-I encodes the fact that rule RI, when antecedent. The difference between reflexives and per- used in its incarnation as the S -- NP VP rule, is pri- sonal pronouns is this. The binding feature REFL marily intended to handle declarative sentences rather is not allowed to inherit onto nodes of certain types than questions. Thus if the answer to Heuristic-1 is (those with CONTROL value [N'rRANS}, thus forc- "no" then this edge is given a higher ranking than if ing the reflexive pronoun to become locally bound. In the answer is "yes". This heuristic, taken together with the case of non-reflexive pronouns, the class of possible others, determines the rank of the edge instantiated antecedents is determined by n,,difying the subcatego- from this rule, which in turn determines the order in rization information on the hel,,l governing the pronoun which edges will be tried. The result in this case is that so that all the subcategorized-fl~r GR's later in gram- for a sentence such as 53 below, the system will pre- matical order than the pronoun are "contra-indexed" fer the reading for which an appropriate answer is ".a with the pronoun (and thereby prohibited front being character in a play by Shakespeare", over the reading its antecedent). Binding then takes place precisely as which has as a felicitous answer "Richard Burton". with reflexives, but somewhere higher in the tree. We illustrate this d~ttttction v,ti, kh I.~O examples. S3. Who is Hamlet? [n sentence S I below told subcategorizes for three con- stituents: the subject NP Pullum, the direct object It should be empha~sized, however, that heuristics Gazdar, and the oblique object PP about himself.' are not an essential part of the system, as are the fea- Thus either PuUum or f;uzdur are po~ible antecedents ture of himself, but not Wasow. passing principles, but rather are used only for reasons of efficiency. In theory all possible constituents SI. Wasow was convinced that Pullum told permitted by the grammar will be found eventually Gazdar about himself. with or without heuristics. The heuristics siu,ply help a linguist tell the parser which readings are most likely, $2. Wasow persuaded Pullum to shave him. and which parsing strategies are usually most fruitful, thereby allowing the parser to construct the most likely [n sentence 52 shave subcategorizes for tile direct reading first. We believe that this clearly diifereuti- object NP him and an NP subject eventue.tly tilled by ares [IPSG from "ad hoc" systems which do not make the constituent Pullum via control. Since the subject sharp the distinction between theoretical principle and position is contra-indexed with tile pronoun, PuUum is heuristic guideline, and that this distinction is an izn- blocked from serving a~ the a,tecedent. The pro,mun portant one if the natural language understanding pro- is eventually bound by the NP WanouJ higher up in the grams of today are to be of any use to the natural tree. language programs and theories of the future.
Heuristics to Optiudze ."Joareh ACKO WLED(4EME1NTS '['he liPS(; system, based as it is upon a care- We would like to acknowledge the valuable assi- fully developed hngui~tic theory, has broad expressive tance of Thomas Wasow ~md Ivan Sag ht tile writing power. In practice, how-ver, much of this power is often of this paper. We would also like to thank Martin Kay not necessary. To exploit this fact the IiPSC, system and Stuart Shi.e~:r lot tlke~r tte[p[u[ cuttutteut, aly on an u.~cs heuristics to help r,,duve the search space implic- earlier draft. itly defined by the grammar. These heuristics allow the parser to produce an optimally ordered agenda of edges to try ba.sed on words used in tile sentence, and on constituents it has found so far.
• The pNpOlltiOll tl treltt~| eel~lttt~tllF :is a c:~se tam'king.
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