| { | |
| "paper_id": "C92-1030", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T12:33:48.704003Z" | |
| }, | |
| "title": "An Empirical Study on Rule Granularity and Unification Interleaving Toward an Efficient Unification-Based Parsing System", | |
| "authors": [ | |
| { | |
| "first": "Masaaki", | |
| "middle": [], | |
| "last": "Nagnfa", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "ATR Interpreting Telephony Research Laboratories", | |
| "institution": "JAI'", | |
| "location": { | |
| "addrLine": "2-2 Hikaridai, Seika-cho, Soraku-gun", | |
| "postCode": "619-{}2", | |
| "settlement": "Kyoto", | |
| "region": "AN" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "This paper describes an empirical study on the optimal granularity of the phrase structure rules and the optimal strategy for interleaving CFG parsing with unification in order to implement an eltlcient unification-based parsing system. We claim that using \"medium-grained\" CFG phrase structure rules, which balance tile computational cost of CI?G parsing and unification, are a cost-effective solution for making unification-based grammar both efficicnt and easy to maintain. We also claim that \"late unification\", which delays unification until a complete CI\"G parse is found, saves unnecessary copies of DAGs for irrelevant subparses and improves performance significantly. The effectiveness of these methods was proved in an extensive experiment. The results show that, on average, the proposed system parses 3.5 times faster than our previous one. The grammar and the parser described in this paper are fully implemented and ased as the .lapmmse analysis module in SL-TRANS, the speech-to-speech translation system of ATR.", | |
| "pdf_parse": { | |
| "paper_id": "C92-1030", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "This paper describes an empirical study on the optimal granularity of the phrase structure rules and the optimal strategy for interleaving CFG parsing with unification in order to implement an eltlcient unification-based parsing system. We claim that using \"medium-grained\" CFG phrase structure rules, which balance tile computational cost of CI?G parsing and unification, are a cost-effective solution for making unification-based grammar both efficicnt and easy to maintain. We also claim that \"late unification\", which delays unification until a complete CI\"G parse is found, saves unnecessary copies of DAGs for irrelevant subparses and improves performance significantly. The effectiveness of these methods was proved in an extensive experiment. The results show that, on average, the proposed system parses 3.5 times faster than our previous one. The grammar and the parser described in this paper are fully implemented and ased as the .lapmmse analysis module in SL-TRANS, the speech-to-speech translation system of ATR.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "Uuifieation-based framework bins been an area of active research in natural language processing. Unification, wbich is the primary operation of ibis frame.work, provides a kind of constraint-checking mechanism for nlerging varioas information sources, sllcb as syntax, semantics, and pragmatics. The computational inefficiency of unification, however, precludes tile development of large practical NLP systems, although the framework has many attractiw~ theoretical properties.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "The efforts made to improve tile efficiency of a uriitication-ba.sed parsing system can be classified into four categories.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "\u2022 CFG parsing algorithm", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "\u2022 Graph unification algorithm", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "\u2022 Granunar representation and organizati(m", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "There bave been well-known efficient CFG parsing algorithms such as CKY [Aho mid UllHnm, 77] , Ear~ ley [Earley, 70] , CtIAffl' (Kay, 80] , eatd I,R [Aho and Ullmaa L 77] ['t'omita, 86] . There have also been several recent in-depth studies into efficient graph unification algoritbms, whose main concerns have been either avoiding irrelevant copies of l)AGs [Karttunen and Kay, 85] [Pereira, 85] [Karttun .... 86] [Wroblewski, 87] [Godden, 90] [Kogure, 90] [Tomabechi, 91] [E1aele, 91] , or the exhaustive expansion of disjunctions into their disjunctive normal forms [Kasper, 87] There has, however, been litth: discussion regarding the optimal representation of a grammar, or linguistic knowledge, in the unification-based framework, from tile engineering point of view. Grammar organization is highly flexible, as tile unification-based framework uses two different forms of knowledge representation; atomic phrase structure rules and feature structure descriptions. Method selection greatly at\" facts both the computational elficieney and the maiutenauce cost of the system. There luL~ also been little discussion regarding optimal interaction between the CFG parsing process and the unification process in unificatlon-based parsing, which also greatly af|~ct~; overall performance.", | |
| "cite_spans": [ | |
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| "start": 72, | |
| "end": 88, | |
| "text": "[Aho mid UllHnm,", | |
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| { | |
| "start": 89, | |
| "end": 92, | |
| "text": "77]", | |
| "ref_id": null | |
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| { | |
| "start": 104, | |
| "end": 112, | |
| "text": "[Earley,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 113, | |
| "end": 116, | |
| "text": "70]", | |
| "ref_id": null | |
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| { | |
| "start": 128, | |
| "end": 133, | |
| "text": "(Kay,", | |
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| { | |
| "start": 134, | |
| "end": 137, | |
| "text": "80]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 149, | |
| "end": 170, | |
| "text": "[Aho and Ullmaa L 77]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 171, | |
| "end": 185, | |
| "text": "['t'omita, 86]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 359, | |
| "end": 378, | |
| "text": "[Karttunen and Kay,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 379, | |
| "end": 382, | |
| "text": "85]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 383, | |
| "end": 392, | |
| "text": "[Pereira,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 393, | |
| "end": 427, | |
| "text": "85] [Karttun .... 86] [Wroblewski,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 428, | |
| "end": 431, | |
| "text": "87]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 432, | |
| "end": 440, | |
| "text": "[Godden,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 441, | |
| "end": 444, | |
| "text": "90]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 445, | |
| "end": 453, | |
| "text": "[Kogure,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 454, | |
| "end": 457, | |
| "text": "90]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 458, | |
| "end": 469, | |
| "text": "[Tomabechi,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 470, | |
| "end": 482, | |
| "text": "91] [E1aele,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 483, | |
| "end": 486, | |
| "text": "91]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 569, | |
| "end": 577, | |
| "text": "[Kasper,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 578, | |
| "end": 581, | |
| "text": "87]", | |
| "ref_id": null | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u2022 Interaction between CFG parsing and unilication", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Here we introduce the notion of granularity, and suggest mcdium-gra~ued phrase structure rules, in which morph~.syutactic specifications in the teature descriptious are expallded into phrase structure rules. We claim that it reduce the computational loads of unification without intractably increasing tim lmulber of rules, and it is optimal ill tile sense that it satis ties both ettleiency and maintainability. We also suggest late unification as another ~lution to tim COl)ylug problem, as it avoids unnecessary copies of irrel evant subparses by delaying unification mttil a COlnph:te CI,'G parse is found.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u2022 Interaction between CFG parsing and unilication", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In tile following sections, the design and iml)lemen tatiun of tim medimn-grained phrase structure rules in explailmd, then the implementation of the late uni: tication is illustrated, anti finally the elfectiveness of the proposed nlethods is proven in experiments. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u2022 Interaction between CFG parsing and unilication", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Phrase structure rule granularity has been introduced to refer to the amount of linguistic constraints specified in the atomic CFG phrase structures rules without annotations. The rule granularity spectrum has been classified into four categories as shown in Table 1 , using the number of grammar rules ms a ruessure.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 259, | |
| "end": 266, | |
| "text": "Table 1", | |
| "ref_id": "TABREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Granularity", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "Unification-based grammars, in general, are characterized by a few general annotated pbrase structure rules, and a lexicon with specific linguistic descriptions. This is especially true for HPSG [Pollard and Sag, 87] and JPSG [Gunji, 87] , which are to be categorized as extremely-coarse grained, as they drastically reduce the nmnber of phrase structure rules into two for English and one for Japanese, respectively. In these frameworks, the only role of the phrase structure rules is to provide a device for combining a head with its complement. Most linguistic constraints are stored in the feature descriptions.", | |
| "cite_spans": [ | |
| { | |
| "start": 195, | |
| "end": 212, | |
| "text": "[Pollard and Sag,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 213, | |
| "end": 216, | |
| "text": "87]", | |
| "ref_id": null | |
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| { | |
| "start": 226, | |
| "end": 233, | |
| "text": "[Gunji,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 234, | |
| "end": 237, | |
| "text": "87]", | |
| "ref_id": null | |
| } | |
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| "eq_spans": [], | |
| "section": "Granularity", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "Coarse-grained rules have been characterized as a grammar consisting of atonfic phrase structure rules with medium constraints, and feature descrip tions with strong constraints. Medium-grained rules have been characterized as a grammar consisting of atomic phrase structure rules witb strong constraints, and feature descriptions with mediuln constraints. Medium-grained rules differ from coarsegrained rules in that they include morpho~syntax in the phrase structure rules, while coarse-grained rules include them in the feature descriptions. This means that medium-grained rules are strong enough to derive syntactic structures from atomic phrase structure rules without feature descriptions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Granularity", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "Grammars for conventional NLP systems using simple or augmented CFG fall into the category of fine-grained rules, which represent most of linguistic constraints as CFG phrase structure rules, and the number of rules usually amounts to an intractable number of several thousands for practical applications.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Granularity", | |
| "sec_num": "2.1" | |
| }, | |
| { | |
| "text": "In unification-based framework, a linguistic constraint cart either be described as atomic context-flee phrase structure rules, or as feature descriptions in annotations and lexical entries. As the number of atomic phrase structure rules decreases, the number of feature descriptions increases. It is true that the lexieo-syntactic approach makes tile granunar modular and improves its maintainability by reducing the number of rules. However, it must be noted that the computational cost of disjunctive feature structure unification, in the worst ease, is exponential in the nmnber of disjunctions [Kasper, 87] , whereas tile cost of CFG parsing is o(N s) in the input length N. Therefore, extreme rule reduction results in inefficiency. This overwhelms the benefits of the maintainability of the reduced number of rules since grammar development is essentially a trial-and-error process and requires a short turn-around time. However, the cost for CFG parsing also increases as the number of rules increases. Therefore, we must chose tile granularity so that the reduction in unification cost outweighs tile increase in CFG parsing cost, in order to gain overall etfieiency.", | |
| "cite_spans": [ | |
| { | |
| "start": 599, | |
| "end": 607, | |
| "text": "[Kasper,", | |
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| "start": 608, | |
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| "text": "87]", | |
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| "section": "Maintainability and Efficiency", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "In this section, we illustrate the difference between \"coarse-grained\" rules and \"medium-grained\" rules using our HPSG-based spoken-style Japanese grarnlnal'S as an exaluple,", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The HPSG-Based Japanese Grammars", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "We have developed two unification-based grammars with different granularity l, which are essentially based on tIPSG and its application to Japanese (JPSG), for the analysis module [Nagata and Kogure, 90] of an experimental Japanese-to-English speech-tospeech translation system (SL-TRANS) [Morimoto et al., 90] .", | |
| "cite_spans": [ | |
| { | |
| "start": 180, | |
| "end": 199, | |
| "text": "[Nagata and Kogure,", | |
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| "start": 200, | |
| "end": 203, | |
| "text": "90]", | |
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| }, | |
| { | |
| "start": 289, | |
| "end": 306, | |
| "text": "[Morimoto et al.,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 307, | |
| "end": 310, | |
| "text": "90]", | |
| "ref_id": null | |
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| "eq_spans": [], | |
| "section": "The HPSG-Based Japanese Grammars", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "We have selected the \"secretarial service of an international conference registration\" as our task domain, in which a conversation between a secretary and a questioner is carried out. Tile Japanese grauunars~ however, ~tre not task-specific, but rather generalpurpose OlleSj which cover a wide range of pllenonl-I Historically speaking, we fil~t developed coarse-grained rules &lid then we nlallllally tl'al|sfonned them into mediumgrldned rules for e|licicncy. ena at ruazly linguistic levels from syntax, and seman tics, to pragmatics using typcd feature structure descriptions. The linguistic phenomena covered in these grammars include:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The HPSG-Based Japanese Grammars", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "\u2022 l,'undamental Constructions: causative, passive, benefactive, negation, interrogative, etc.,", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The HPSG-Based Japanese Grammars", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "\u2022 Control and Gaps: subject/object control,", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The HPSG-Based Japanese Grammars", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "\u2022 Unbounded Dependencies: topic, relative,", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The HPSG-Based Japanese Grammars", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "\u2022 Word Order Variation and Ellipsis.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The HPSG-Based Japanese Grammars", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "The coarse-grained HPSG-based Japanese grammar has about 20 generalized phrase structure rules, while the medium-grained grarmnar has about 200 phrase structure rules. Both gra, lnmars use the same lexicon with a vocabulary of about 400. ~", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Coarse-Grained Rules vs. Medium-Grained Rules", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "In the coarse-grained grarmnar, phrase structure rules only refer to the relative position l)etween the five basic syntactic categories for Japanese: verb (V), noun (N), adverb (ADV), postposition (P), and attributive (ATT). Most of the specific linguistic information is encoded as feature descriptions in either the annotation of the l)hrase structure rules or the lexical entries. In principle, there is no distinction as to whether a constituent is lexical or phrasal, and no subcategories of the 5 basic categories. This contributes greatly to the reduction in the numbcr of phrase structure rules, which results in better grammar maintainability. We present all the phrase structure rules of the coarse-grained Japanese grammar in Appendix A.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Coarse-Grained Rules vs. Medium-Grained Rules", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "It has been noticed that the extensive use of dis-junctions in feature descriptions, which results from the reduction of the number of phrase structure rules, is the main cause of incfficieney in the coarse-grained version of the grammar. The three major sources of disjunctions are, lnorpho-syntactic specifications for diverse expressions in the final part of the sentence, frec word order and ellipsis of verb complements (subeat slash scrambling), and semantic interpretation of deep case and aspect, where the first two particularly are the problems in spoken-style Japanese.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Coarse-Grained Rules vs. Medium-Grained Rules", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "We have manually converted the coarse-grained phrase structure rules into medium-grained rules to reduce thc computational cost of unilication. First, we divided each of the basic categories into several subcategories. Then, we divided the coarse-grained phrase structure rules according to the subcategorics. qb kee I) the grammar readable, however, we choose to leave the subcat slash scrambling and the semantic 2We also }lave aalother vel~iOll of tile gF~nlllal\" for tile sam,: interpretation undone, and nmde extensive efforts to expand the morpho-syntactic speeificatioas.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Coarse-Grained Rules vs. Medium-Grained Rules", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "In this section, we illustrate the process of transfof marion using a predicate verb l)hrasc production rulc as an example. Japanese predicate phrases consist of a main verb followed by a sequence of auxiliaries azld sentence final particles. There is an ahnost ottodimensional order of verbal constituents such as in Figure l , which reflects the basic hierarchy of the J apanese sentence structure.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 318, | |
| "end": 326, | |
| "text": "Figure l", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Example: Medium-Grained Rules for Predicate Verb Phrases", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Kernel verbs occur first in a predicate phrase sequence. Voice auxiliaries precede all other auxiliaries, and within this category, the causative auxiliary (sa)se,'u precedes the passive auxiliary (ra)re~t. Aspect auxiliaries, such a.s the progressive auxiliary (Ie)ivu precede modal auxiliaries ;rod follow voice auxiliaries. Modal auxiliaries are classified into two groups with respect to the relative order of negative and tense auxiliaries. Mood1 iuehldes the optative arlxiliaries, such as tai (want), beki (should/must), etc. Mood2 includes the evidential or inferential auxiliarics such as rashii (seem/look), kamoshirenai (may), etc. Negative auxiliaries uai, u (not) follow voice, aspect, and mood l auxiliaries, and precede tense and mood2 auxiliaries. Tease auxiliaries la, da (-ed) show irregular behavior. They follow the voice, aspect, mood1, and negative auxiliaries, and precede the mood2 auxiliaries. They also can tollow the mood2 auxiliaries.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Example: Medium-Grained Rules for Predicate Verb Phrases", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "in the coarse-grained grammar, we provide a single phrase structure rule for the phcnomena.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Example: Medium-Grained Rules for Predicate Verb Phrases", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "The order constraints between auxiliaries are specified in the annotution of rule (1) and each lexi cal entry by the combination of tile syntactic features, such as the synlheadlsubcat for preceding constituents, tile synlheadlcoh for following constituents, and the syn[headlroodl for the position of the con stituent~ in the verb phrase hierarchy. For example, the causative auxiliary verb sern has the following, feature bundles in its syn{headlmodl feature.", | |
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| "eq_spans": [], | |
| "section": "v~(v AUXV) O)", | |
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| { | |
| "text": "[", | |
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| "section": "v~(v AUXV) O)", | |
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| { | |
| "text": "[CAUS +] [DEhC -1 [hSPC -] [DONT -] [OPTT -] [IEGT -] [PhST -] lEWD -] [TENT -] [PONT -] [POLT-AUX -] [INT~ -] [SFP-I -] [SFP-2 -] [SFP-3 -]]", | |
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| "section": "v~(v AUXV) O)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In converting the rule, first we have claasitied the verbal phrasal categories according to the hierar. ehy, e.g. V-kernel, V-aspect, V-moodl, V-negt, V-mood2~ and V-tease, then we have subcategorized the auxiliari~ as shown in Table 2 . Thus, the coarsegrained phrase structure rule (1) is converted to the 32 medium-grained grammar rules in Appendix B kernel < voice < aspect < moodl < negate < tense < mood2 < tense (sa)seru (te)iru tai nai ta rasii ta (ra)reru (te)morau tagaru n da desu u masu darou Unification is an expensive operation, so the point of evaluating feature descriptions during CFG parsing has serious affects on the overall performance. We have implemented two strategies for feature description evaluation:", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 228, | |
| "end": 235, | |
| "text": "Table 2", | |
| "ref_id": "TABREF2" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "v~(v AUXV) O)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Early Unification (Step-by-step Strategy)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "v~(v AUXV) O)", | |
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| }, | |
| { | |
| "text": "Featnre descriptions are evaluated step-by-step, at each rule invocation in the CFG parsing.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "v~(v AUXV) O)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Late Unification (Pipeline Strategy) Feature descriptions are evaluated when a complete CFG parse is found. The \"well-formedness\" of a parse derived from atomic CFG rules is verified by evaluating associated feature descriptions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "v~(v AUXV) O)", | |
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| { | |
| "text": "The granularity of the phrase structnre rules is closely related to the proper selection of the evaluation strategy. Since the atomic phrase structure rules ill the coarse-grained grammar are not so strong as to constrain syntactic structures, we have to employ the early unification to avoid a nnmber of irrelevant subparses which should have been eliminated by the evaluation of annotations. IIowever, since the atomic rules in the medium-grained grammar have detailed morpho-syntax specifications, they should be able to avoid irrelevant copies by using the late unification.", | |
| "cite_spans": [], | |
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| "section": "v~(v AUXV) O)", | |
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| { | |
| "text": "We have implemented the various evaluation strategies by doing additional housekeeping in the underlying parser. The parser used here is called the Typed [Kogure, 89] , which is based on the active chart parsing algorithm [Kay, 80] and typed feature structure unification 86] .", | |
| "cite_spans": [ | |
| { | |
| "start": 154, | |
| "end": 162, | |
| "text": "[Kogure,", | |
| "ref_id": null | |
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| { | |
| "start": 163, | |
| "end": 166, | |
| "text": "89]", | |
| "ref_id": null | |
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| { | |
| "start": 222, | |
| "end": 227, | |
| "text": "[Kay,", | |
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| { | |
| "start": 228, | |
| "end": 231, | |
| "text": "80]", | |
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| { | |
| "start": 272, | |
| "end": 275, | |
| "text": "86]", | |
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| "section": "Implementing the Evaluation Strategies", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "Tile active chart parser and the unification algorithm are implemented in C on Sun4, which is a 10-MIPS work station. The unification algorithm is based oil nondestructive graph unification [Wroblewski, 87 ], which we extend to treat negation, loop, type symbol subsumption relationships, and disjunctiou. Successive approximation [Kasper, 87] is used for disjunctive feature structure unification.", | |
| "cite_spans": [ | |
| { | |
| "start": 190, | |
| "end": 202, | |
| "text": "[Wroblewski,", | |
| "ref_id": null | |
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| { | |
| "start": 203, | |
| "end": 205, | |
| "text": "87", | |
| "ref_id": null | |
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| { | |
| "start": 331, | |
| "end": 339, | |
| "text": "[Kasper,", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 340, | |
| "end": 343, | |
| "text": "87]", | |
| "ref_id": null | |
| } | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Implementing the Evaluation Strategies", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "The Active chart parsing algorithm basically consists of chart initialization and iterative rule invocation. The basic part of the iterative rule invocation is shown in Figure 2 . AcpContinue checks the suspending condition and calls rule invocation recursivcly. AcpOneStep carries out a cycle of rule invocation which consists of getting a new pending edge (GetPendingEdge), adding it to the chart (AddEdge), combining active and inactive edges (TryToContinue-ActiveEdge/TryToContinuelnactiveEdge), and proposing new edges (ProposeProductions). The parser stops (SatisfySuspendingCondition?) when it finds an inactive edge whose starting and ending vertex are the left-most and right-most vertex of the chart, respectively, and whose label is the start symbol of the granunar.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 169, | |
| "end": 177, | |
| "text": "Figure 2", | |
| "ref_id": "FIGREF2" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Implementing the Evaluation Strategies", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "In early unification, the feature descriptions are evaluated when the edges are combined, while in late unification, they are evaluated iu the chart suspending condition check only if tile clmrt suspending condition bolds. Delaying unification is implemented by adding a slot edge.parse to the edge structure, which keeps a list of the pair of active aud inactive edges constructing the edge. If either or both of the argument feature structures of the unification have not been evaluated, they are recursively evaluated to get the target feature structure.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Implementing the Evaluation Strategies", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "It has to bc j~oted that some derivations that termhmte when feature descriptions are evaluated, may not terminate if they are ignored. For example, it is possible to write a rule for unbounded dependency like (2), in which m~ element in tile subcat feature is moved to the slash feature, to introduce slashed categories dynamically 3.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Implementing the Evaluation Strategies", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
| "raw_str": "--~ (~)", | |
| "eq_num": "(2)" | |
| } | |
| ], | |
| "section": "Implementing the Evaluation Strategies", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "Ignoring feature descriptions in the rule may cause aal infinite loop. Therefore, feature descriptions arc forced to be evaluated, when rules that cause a loop are encountered in late unification.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Implementing the Evaluation Strategies", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "Tile effectiveness of the strategies proposed in this paper call be judged by observing their behavior in practice. We have tested the time behavior of parslug with respect to rule granularity and interleaving strategy of CFG parsing and unification. 85 sample sentences are used. These are selected from the sample subcorpus of ATR's dialogue corpus whose teu~k dornain is the \"secretarial service of an international conference\". The average length of the sample sentences is 11.0 characters, and their maximum and minimum length are 2 and 28 characters, respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Experiment", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We have developed two 3 al)anese grammars of different granularity with ahnost the same coverage. The coarse-grained rules consist of 22 generalized phrase structure rules with detailed ti~.ature description ill their annotations, while the medium-grained rules consist of 164 detailed phra.se structure rules with less detailed feature descriptions. Both grammars use the same lexicon with about 400 lexical cn tries. We haw: ,also implemented two different fca ture descrilrtion evaluation modes in tile active chart parser. 'file early unificalion cwdurdion mode evah> ates tile feature descriptions at each rule application (tile step-by-step strategy). The late uniJica*io, cval-ualio~ mode, on the other hand, delays unification until a CUlnl)lete syntactic structure is tk~und lly using the atomic phrase structure rules only (tile pipelin,~ strategy).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Experiment", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The average parsing tilne is shown ill Table : 1 It shows that, on average, tile m(~diun>grained glum x A", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 39, | |
| "end": 46, | |
| "text": "Table :", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Experiment", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "x Figure 3 : Comparison of Coarse-Grained ILules and Medimn-(-;rained Rules mar rules are 1.7 times more elllcient than the coarse grained rules ill the early unit|cation mode, and that tile late unification mode is 2.0 times more etncient than tile early unilieation mode with the medium~ grained gramniar. Moreover, when tile mediuulgrained grauunar rules and tile late unification mode m'e combined, tile new parser runs 3.5 times fmqter than till', l/revklus olle using the coarse-grained grammar rules and the early unities|ion. 4", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 2, | |
| "end": 10, | |
| "text": "Figure 3", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Experiment", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The relationship between inlmt length and 1)arsing time with resl)CCt to grammar gramllarity is shown ill Figure 3 . Ill general, tim medimn grained ru/e.s pertormed bet, ter than the coarse grained rules. This 13:lldency beCOllles dearer, *is tile 8elltellee lellg].h ill~ crelmes. This reslllLs from the redllction of disjunc.. tiw! feature descriptions whose computational cost in-4\"1'}m rq~l)l-Oach of st~Villg illllleCc~ssilay c:ol}i(~s for ill-d~!V~tla sub}Dalai.-~, ]it a iI,%i.~]llg ptot:ess |)~,. l&tc IIIlil[c#tt]oll is el thogonat| [tl t ~le a]lpl o~;hes ,}f saving illtll{:CeSS&ly c~pieS within a unifieslion in'acess, such a_s [T,mla[mchi, 9@ Thelcfmv, the ctfects (ff speed up citii be multiplied. We haw~ gdte;tdy impleme|~ted his (lll~kqi d\u00a2:stltlCtiVe g~l,~l)ll Iltli|i(:atiOll, &lid the l)l*~|illlillal y {:XpeF ilIl~lll l('Slllt shows that th,: ])~l*er witll new ulli|iel\" rlltls a[lllOSl t wh:c am ta~st *Ls the one t'el)orte(| ill this l);tl)el-. However, we occasionally encounter sentences which are parsed faster using coarse-grained rules rather than medium-grained rules. This is because the increase in the atomic CFG parsing cost exceeds the reduction of tbe unification cost. The relationship between input length and parsing time with respect to unification evaluation mode is shown in Figure 4 . This shows that the late unification mode is significantly more efficient than the early unification mode. It also shows that the parsing time in the late unification mode seems to be lmlynomial (not exponential) in the input length, while that in the early unification mode varies widely and irregnlarly. This is because the parsing time in tile late unification mode is mainly predominated by the cost of atomic CFG parsing by dclaying unification, whereas the parsing tiule in the early unification mode is mainly predominated by the cost of unification.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 106, | |
| "end": 114, | |
| "text": "Figure 3", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1316, | |
| "end": 1324, | |
| "text": "Figure 4", | |
| "ref_id": "FIGREF4" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Discussion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We have demonstrated tbc effectiveness of combining medium-grained phrase structure rules with late unification. Experiment results suggest that new prospective techniques for speeding up ratificationbased parsing exist.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Discussion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The first is automatic transformation of phrase structure rules, which converts disjunctions in the feature descriptions into atomic phrase structure rifles. Some disjunctions such as subcat slash scrambling are so reguhu\" that it seems possible to exl)and them into a set of CF(; rules using forlnal i)rocedures. If the grammar compiler can perform this kind of transformation automatically, we can gain efficiency without losing grammar maintainability.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Discussion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The second is feature-sensitive lazy unification. Unilication is used for both huihling up a structure using infornratiou-propugat, ion and blocking rule a Fplication using constraint-checking. If the grammar compiler can separately output those features for constraint-checking such ~ syntactic llcatnrcs, and those for information-propagation such as semantic rel)resental;ions , irrelevant subparses can be pruned efficiently by evaluating the constraint-checking features first. Unification is an associative and comnm-tative operation, so the same results from the featuresensitive lazy unification are assured.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Discussion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The third is parallel implementation of a unification-based parser based on late unification (pipe-line strategy). In early unification (step-by-step strategy), it is hard to perform parsing in parallel because the CFG parsing process and the unification process depend strongly on each other. However, both processes are completely separated in the pipe-line strategy . Therefore, it is easy to introduce the existing parallel algorithms to both CFG parsing and unification. It is estimated that most feature descriptions can be evaluated in parallel, at least, at the ]exical level, because unification-based grammars such as IIPSG derive phrase structure by iteratively propagating the local constraints.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Discussion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "]n this paper, we have proposed two techniques for implementing an efficient unification-based parsing system, which, when combined, significantly improve the overall performance. The first is changing the granularity of the context-free phrase structure rules into medium-grained rules. This enables us to reduce the amount of unification for feature descriptions without intractably increasing the number of phrase structure rules. The second is late unification in which the unification for feature descriptions is delayed until a complete CFG parse is found. This saves unnecessary copies of feature structures which are wasted for irrelevant subparses.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "We have tested the time behavior of the parsing system using two granunars of different granularity (coarse/medium) and two different strategies for invoking unification (early/late). It is proved that, on average, late unification using medium-grained rules parses 3.5 times fester than the previous early unification using coarse-grained rules.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "ACITe~q DE COLING-92. NANTES, 23-28 ^ofrr 1992", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "PROC. OF COLING-92, NANTES, AUG. 23-28, 1992", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "The author would like to thank Dr. l(urematsu, and all the n,embers of A2'R huerpreting q'elephony Besearch I,al)s. for their constant help m)d fl'tfitflfl discussions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Acknowledgments", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A Coarse-grained Gramn,ar Rules for JapaneseThe ll~k[l/e of each rule is showll ill ILhe cOlnlllellt~ where t.he su[t~x -al L -ch, -coord means adjm~ction, COnlplenmntation. aim coordination, respectively.; w-at=ah it -> (p it); n-lt~coord ;;; Complox Ithd Coral)erred IJoult ~* -> (nprefix zt) The coarse-grained grammar rule (1) is converted to the following 32 medium-grMned ~;rammar rules.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Appendix", | |
| "sec_num": null | |
| } | |
| ], | |
| "bib_entries": { | |
| "BIBREF0": { | |
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| "FIGREF0": { | |
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| }, | |
| "FIGREF1": { | |
| "uris": null, | |
| "num": null, | |
| "text": "8t|bCOll)lls, whlcll is nsed for tile continuous speech l'ecognition module [Takez.awa el, ~xl. , 911. It only imea atoaalc CFG la31esp a31d the ]lulll}~r of rules ~llOUll{S to Inol~ thall 2,0(~, It is, thcrefore~ categolJzed ~-s a tin,grained gr~Htnar in our defiltition.", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "uris": null, | |
| "num": null, | |
| "text": "Iterative Rule Invocation in an Active Chart Parsing Algorithm Feature Structure Propagation Parser (TFSP Parser)", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF3": { | |
| "uris": null, | |
| "num": null, | |
| "text": "Sin o/u. implelnentation, for e|liciency l'e~ualls, w~: gellotal~\" all the approl)fiate combinations of sul)cat and sl:tsl~ in ~ul V&IICe, atld kee I) thertl ~.s a disjunctive fe~tttll'e st rttcLiil'O Aortas DE COTING-92, NANTrS, 23-", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF4": { | |
| "uris": null, | |
| "num": null, | |
| "text": "Comparison of Early Unification and Late Unificationcreases exponentially in the number of disjunctions.", | |
| "type_str": "figure" | |
| }, | |
| "TABREF0": { | |
| "content": "<table><tr><td/><td>Constraints in Phrase</td><td>Constraints in</td><td/><td>Nmnber of Phrase</td></tr><tr><td>Structure Rules</td><td>Structure ]Eules</td><td colspan=\"2\">Feature Descriptions</td><td>Structure Rules</td></tr><tr><td>Extremely-Coarse-Grained</td><td>weak</td><td>very strong</td><td>.</td><td>1 ~ 10</td></tr><tr><td>Coarse-Grained</td><td>medium</td><td>strong</td><td/><td>10 ~ 100</td></tr><tr><td>Medium-Grained</td><td>strong</td><td>medimn</td><td/><td>100 ~ 1000</td></tr><tr><td>Fine-Grained</td><td>very strong</td><td>weak</td><td/><td>1000</td></tr><tr><td colspan=\"2\">2 The Granularity of Phrase</td><td/><td/><td/></tr><tr><td>Structure Rules</td><td/><td/><td/><td/></tr></table>", | |
| "type_str": "table", | |
| "html": null, | |
| "text": "Granularity of phrase structure rules characterized by tile number of rules and the strength of linguistic constraints in tile phrase structure rules aJtd the feature descriptions", | |
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| "TABREF2": { | |
| "content": "<table><tr><td/><td colspan=\"2\">: Subcategories of auxiliaries in the medium-</td></tr><tr><td colspan=\"2\">grained grammar</td><td/></tr><tr><td colspan=\"3\">4 Interleaving CFG Parsing</td></tr><tr><td/><td>and Unification</td><td/></tr><tr><td>4.1</td><td>Strategies for Evaluating</td><td>Feature</td></tr><tr><td/><td>Descriptions</td><td/></tr></table>", | |
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| "content": "<table><tr><td>procedure AcpContinue(chart)</td></tr><tr><td>begin</td></tr><tr><td>if SatisfySuspendingCondition?(chart)</td></tr><tr><td>then return chart</td></tr><tr><td>else</td></tr><tr><td>AcpContinue(AcpOneStep(chart))</td></tr><tr><td>end</td></tr><tr><td>procedure AepOneStep(chart)</td></tr><tr><td>begin</td></tr><tr><td>pendingedge = GetPendingEdge(chart)</td></tr><tr><td>AddEdge(pendingedge)</td></tr><tr><td>if EdgeActive?(pendingedge) then</td></tr><tr><td>TryToContinueActiveEdge(pendingedge, chart)</td></tr><tr><td>else</td></tr><tr><td>TryToContinuelnactiveEdge(pendingedge, chart)</td></tr><tr><td>ProposeProductlons(pendingedge)</td></tr><tr><td>return chart</td></tr><tr><td>end</td></tr></table>", | |
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| "html": null, | |
| "text": "", | |
| "num": null | |
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| "TABREF4": { | |
| "content": "<table><tr><td>~ 0</td><td/></tr><tr><td>I:</td><td>\u00d7 co~e, Eady ^ Mmdlum, Early</td></tr><tr><td>260</td><td/></tr><tr><td/><td>\u00d7 \u00d7</td><td>\u00d7</td></tr><tr><td/><td>\u00d7 x a</td><td>^</td></tr></table>", | |
| "type_str": "table", | |
| "html": null, | |
| "text": "Aw~ragc parsing time with respect to grailularity and unification mode", | |
| "num": null | |
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| } | |
| } | |
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