| { | |
| "paper_id": "H91-1044", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:33:04.914678Z" | |
| }, | |
| "title": "Parsing the Voyager Domain Using Pearl", | |
| "authors": [ | |
| { | |
| "first": "David", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Magerman", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "CIS Department University of Pennsylvania Philadelphia", | |
| "location": { | |
| "postCode": "19104", | |
| "region": "PA" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mitchell", | |
| "middle": [ | |
| "P" | |
| ], | |
| "last": "Marcus", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "CIS Department University of Pennsylvania Philadelphia", | |
| "location": { | |
| "postCode": "19104", | |
| "region": "PA" | |
| } | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "This paper* describes a .al, ural language p~rsi.g algorithm &)r unresu'icl,ed I,ext which uses a probabilhq-hased scoring funct,iow I,o sele(:l, I,he \"besC: parse of ~/ sent,enos acc:ording t,c~ a given gra0nunar. The parser~ \"Pearl~ ix a i,hne-asynciironous t)ol,l,orn-u 1) chart, parser with Earley-l,ype I,Ol)-dowil t)redic:l,ion which pursues I", | |
| "pdf_parse": { | |
| "paper_id": "H91-1044", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "This paper* describes a .al, ural language p~rsi.g algorithm &)r unresu'icl,ed I,ext which uses a probabilhq-hased scoring funct,iow I,o sele(:l, I,he \"besC: parse of ~/ sent,enos acc:ording t,c~ a given gra0nunar. The parser~ \"Pearl~ ix a i,hne-asynciironous t)ol,l,orn-u 1) chart, parser with Earley-l,ype I,Ol)-dowil t)redic:l,ion which pursues I", | |
| "cite_spans": [], | |
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| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "All natural language grammars are ambiguous. Even tightly fitting natural language grammars are ambiguous in some ways. Loosely fitting grammars, which are necessary for handhng the variability and complexity of unrestricted text and speech, are worse. The standard technique for dealing with this ambiguity, prtming grammars by hand, is painful, time-consuming, and usually arbitrary. The solution which many people have proposed is to use stochastic models to train statistical grammars automatically from a large corpus.", | |
| "cite_spans": [], | |
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| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Attempts in applying statistical techniques to natural language parsing have exhibited varying degrees of success. These successful and unsuccessful attempts have suggested to us that: 2Thc grammar uscd for o~tr cxpcrimcnts is the string grammar used in U nisys: P U NDIT natural languagc undcrstanding systcm.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 In order for stochastic techniques to be ett~ctive, they must be applied with restraint (poor estimates of context axe worse than none[6|).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Interactive, interlea~'ed architectures axe preferable to pipeline architectures in NLU systems, because they use more of the available information in the decision-malting process.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTRODUCTION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We have constructed a stochastic parser, \"Pearl, which is based on these ideas.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "INTRODUCTION", | |
| "sec_num": null | |
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| "text": "The development of the Pearl parser is an ettbrt to combine the statistical models developed recently into a single tool which incorporates all of these models into the decision-making component of a. parser. While we hax'e only attempted to incorporate a few simple statistical models into this parser, Peaxl is structured in a way which allows any number of syntactic, semantic, and other knowledge sources to contribute to parsing decisions. 'l'he current implementation of Pearl uses Church's part-of-speech assignment trigram model, a simple probabilistic unknown word model, and a conditional probability model for grammar rules based on part-of-speech trigrams and parent rules.", | |
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| "section": "INTRODUCTION", | |
| "sec_num": null | |
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| "text": "By combining multiple knowledge sources and using a chartparsing framework, Pearl attempts to handle a number of difficult problems. Pearl has the capability to parse word lattices, an ability which is useful in recognizing idioms in text processing, as well as in speech processing. The parser uses probabilistic training from a corpus to disambiguate between grammatically acceptable structures, such as determining prepositional phrase attachment and conjunction scope. Finally, Pearl ms|mains a well-formed subs|ring table within its chart to allow for partial parse retrieval. Partial parses are useful both for error-message generation and for processing ungrammatical or incomplete sentences.", | |
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| "section": "INTRODUCTION", | |
| "sec_num": null | |
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| "text": "For preliminary tests of Pearl's capabilities, we are using the Voyager direction-finding domain, a spoken-language system developed at MiT. 3 We have selected this domain for a number of reasons. First, it exhibits the attachment regularities which we are trying to capture with the context-sensitive probability model. Also, since both MIT and Unisys have developed parsers and grammars for this domain, there are existing parsers with which we can compare 7Pearl. Finally, pearl's dependence on a parsed corpus to train its models and to deri~ its grammar 3Spccial thanks to Victor Zuc at MIT for thc use of thc speech data from MIT:s Voyagcr system. required that we use a domain for which a parsed corpus existed. A corpus of 1100 parsed sentences was generated by the Unisys' I-'I.tNDIT Language Understanding System. These parse trees were evaluated to be semantically correct by PUNDIT'S semantics component, although no hand-verification of this corpus was performed. PUNDIT'S parser uses a string grammar with many comphcated, hand-generated restrictions. The goal of the experiments we performed was to reproduce (or improve upon) the parsing accuracy of PUNDIT USing jUSt the context-free backbone of the PIINDIT grammar, without the hand-generated restrictions and, equally important, without the benefit of semantic analysis.", | |
| "cite_spans": [ | |
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| "start": 141, | |
| "end": 142, | |
| "text": "3", | |
| "ref_id": "BIBREF3" | |
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| "section": "INTRODUCTION", | |
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| "text": "In a. test on 40 Voyager sentences excluded from the training material, Pearl has shown promising results in handling partof-speech assignment, prepositional phrase attachment, and unknown word categorization. Pearl correctly parsed 35 out of 40 or 87.5% of these sentences, where a correc~ parse is defined to mean one which would produce a correct response from the Voyager system. We will describe the details of this experiment later.", | |
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| "section": "INTRODUCTION", | |
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| "text": "In this paper, we will first explain our contribution to the stochastic models which axe used in Pearl: a context-free grammar with context-sensitive conditional probabilities. Then, we will describe the purser's architecture and the parsing algorithm. Finally, we will gi~m the results of experiments we performed using Pearl which explore its capabilities.", | |
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| "section": "INTRODUCTION", | |
| "sec_num": null | |
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| "text": "Recent work involving context-~ee and context-sensitive probabilistic grammars provide httle hope for the success of processing unrestricted text using probabilistic techniques. Works by Chitrao and Grishman [3] and by Sharman, Jehnek, and Mercer[Ill exhibit accuracy rates lower than 50% using supervised training. Supervised training for probabilistic CFGs requires parsed corpora., which is very costly in time and maa-power [2] .", | |
| "cite_spans": [ | |
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| "start": 208, | |
| "end": 211, | |
| "text": "[3]", | |
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| "start": 428, | |
| "end": 431, | |
| "text": "[2]", | |
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| "section": "USING STATISTICS TO PARSE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In our investigations, we have made two observations which attempt to explain the lack-luster performance of statistical parsing techniques:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "USING STATISTICS TO PARSE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Simple probabilistic CFGs provide generalinformation about how likely a construct is going to appear anywhere in a sample of a language. This average likehhood is often a poor estimate of probability.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "USING STATISTICS TO PARSE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Parsing algorithms which accumulate probabilities of parse theories by simply multiplying them over-penalize infrequent constructs.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "USING STATISTICS TO PARSE", | |
| "sec_num": null | |
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| "text": "Pearl avoids the first pitfall by using a context-sensitive conditional probabihty CFG, where context of a theory is determined by the theories which predicted it and the part-of-speech sequences in the input sentence. 'lb address the second issue, Pearl scores each theory by using the geometric mean of the contextual conditional probabilities of all of the theories which have contributed to that theory. This is equivalent to using the sum of the logs of these probabilities.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "USING STATISTICS TO PARSE", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In a very large parsed corpus of English text, one finds that the most frequently occurring noun phrase structure in the text is a noun phrase containing a determiner followed by a noun.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Simple probabilistic CFGs dictate that, given this information, \"determiner noun\" should be the most likely interpretation of a noun phrase. Now, consider only those noun phrases which occur as subjects of a sentence. In a given corpus, yon might find that, pronouns occur just as frequently as \"determiner nolm\"s in the subject position. This type ~fff information can ea~ily be captured by conditional probabilities.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
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| { | |
| "text": "Finally, assume that the sentence begins with a pronoun followed by a verb. In this case, it, is quite clear that, while yon can probably concoct a sentence which fits this description and does not have a pronoun for a subject, the first theory which yon should pursue is one which makes this hypothesis.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The context-sensitive conditional probabilities which \"Pearl uses take into account the immediate parent of a theory 4 and the part-of-speech trigram centered at the beginning of the theory.", | |
| "cite_spans": [], | |
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| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
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| { | |
| "text": "For example, consider the sentence:", | |
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| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
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| { | |
| "text": "My first love was named 'Pearl.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CFG with context-sensitive conditional probabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A theory which tries to interpret \"love\" as a verb will be scored based on the part-of-speech trigram \"adjective verb verb\" and the parent theory, probably \"S --+ NP VP.\" A theory which interprets \"love\" as a noun will be scored based on the trigram \"adjective noun verb.\" Although lexical probabilities favor \"love\" as a verb, the conditional probabilities will heavily favor \"love\" a.~ a noun in this context. '5", | |
| "cite_spans": [], | |
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| "section": "(no subliminal propaganda intended)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "According to probability theory, the likelihood of two indepcndcnl, events occurring at, the same time is the product of their individual probabilities. Previous statistical parsing techniques apply this definition to the cooceurrence of two theories in a parse, and claim that the likelihood of the two theories being correct is the product of the probabilities of the two theories.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This application of probability theory ignores two vital observations about the domain of statistical parsing:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Two constructs occurring in the same sentence are not, necessarily independent (and frequently are not). If the independence assumption is violated, then the product of individual probabilities has no meaning with respect to the joint probability of two event, s.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Since statistical parsing suffers from sparse data, probability estimates of low frequency events will usually be inaccurate estimates. Extreme underestimates of the likelihood of low frequency events will produce misleading joint probability estimates.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
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| { | |
| "text": "4Tl,e parent of a theory is defined as a theory with a CF rule which contains the left-hand side of the theory. For instance, if ~S ~ NP VP\" and \"NP --* det o\" are two grammar rules, the .first rule can be a parent of the secoud~ sittce the left-hand side of the second \"NP\" occurs in the right-hand side of the frst rule.", | |
| "cite_spans": [], | |
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| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "5In fact, the part-of-speedt tagging model wlddt is also used in \"Pearl will heavily favor \"love\" as a noun. We ignore this behavior to demonstrate the benefits of the trlgram conditioning.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
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| "text": "From these observations, we have determined that estimating joint probabilities of theories using individual probabilities is too difficnlt with the available data. We have fonnd that the geometric mean of these probability estimates provides an accurate assessment of a theory's viability.", | |
| "cite_spans": [], | |
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| "section": "Using the Geometric Mean of Theory Scores", | |
| "sec_num": null | |
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| "text": "In a departnre from standard practice, and perhaps against better judgment,we will include a precise description of the theory scoring fimction used by Pearl. This scoring fimction tries to solve some of the problen~ noted in previous attempts at probabilistic parsing [3] [11] :", | |
| "cite_spans": [ | |
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| "start": 269, | |
| "end": 272, | |
| "text": "[3]", | |
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| "start": 273, | |
| "end": 277, | |
| "text": "[11]", | |
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| "section": "The Actual Theory Scoring Function", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Theory scores should not depend on the length of the string which the theory spans.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The Actual Theory Scoring Function", | |
| "sec_num": null | |
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| { | |
| "text": "\u2022 Sparse data. (zero=frequency events) and even zero=probability events do occur, and shonld not resnlt in zero scoring theories.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The Actual Theory Scoring Function", | |
| "sec_num": null | |
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| { | |
| "text": "\u2022 Theory scores should not discriminate against unlikely con= structs when the context predicts them.", | |
| "cite_spans": [], | |
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| "section": "The Actual Theory Scoring Function", | |
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| "text": "In this discnssion, a theory is defined to be a partial or complete syntactic interpretation of a word string, or, simply, a parse tree. The raw score of a theory, 0, is calculated by taking the product of the conditional probability of that theory's CFG rule given the context, where context is a part-of-speech trigram centered at the beginning of the theory and a parent theory's rule, and the score of the contextnal trigram:", | |
| "cite_spans": [], | |
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| "section": "The Actual Theory Scoring Function", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "SCram(0) = .p(rulcol(poPtP2), rulcparent)Sc(poplp2)", | |
| "cite_spans": [], | |
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| "section": "The Actual Theory Scoring Function", | |
| "sec_num": null | |
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| "text": "Here, the score of a trigram is the prodnct of the mutna] information of the part-of-speech trigram, 6 P0PlP2, and the lexical probability of the word at the location of Pi being assigned that part-of-speech Pi .7 In the case of ambiguity (part-of-speech ambignity or multiple parent theories), the maximnm valne of this product is used. The score of a partial theory or a complete theory is the geometric mean of the raw scores of all of the theories which are contained in that theory.", | |
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| "section": "The Actual Theory Scoring Function", | |
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| "text": "Theory Length Independence This scoring fimction, although heuristic in derivation, provides a method for evaluating the value of a theory, regardle~ of its length. When a rule is first, predicted (Earley-style), its score is just its raw score, which represents how mnch the context predicts it. However, when the parse process hypothesizes interpretations of the sentence which reinforce this theory, the geometric mean of all of the raw scores of the rule's snbtree is nsed, representing the overall likelihood of the theory given the context of the sentence.", | |
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| "text": "Low-freqnency Events Although some statistical natural langnage applications employ backing-off estimation techniqnes [10] [5] to handle low-frequency events, 'Pearl uses a very simple estimation technique, reluctantly attributed to Church [6] . This techniqne estimates the probability of an event by adding 0.5 to ev-6The mutual information of a part-of-speech trigrnm, poPlP2, is defined to be ..pry ~w') . where x is any part-of-sr)eech. See [4] for further \"M( pilxp~)'P( ~t ) ) ex planation.", | |
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| "text": "7The trigram scoring [traction actually used by the parser is somewhat more complicated than this. ery frequency count. 8 Low-scoring theories will be predicted by the Earley-style parser. And, if no other hypothesis is suggested, these theories will be pursued. If a high scoring theory advances a theory with a very low raw score, the resulting theory's score will be the geometric mean of all of the raw scores of theories contained in thkt theory, and thus will be much higher than the low-scoring theory's score. Since this sentence is syntactically a.mbiglmns, if the first hypothesis is tested first, the parser will interpret this sentence incorrectly.", | |
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| "text": "However, this will not happen in this domain. Since \"fruit flies\" is a conmmn idiom in insect studies, the score of its trigram, noun noun verb, will be much greater than the score of the trigram, noun verb verb. Thus, not only will the lexical probability of the word \"flies]verb\" be lower than that, of \"flies/norm,\" but also the raw score of \"NP ~ noun (fruit)\" will be lower than that, of \"NP ~ norm noun (fruit flies),\" because of the differential between the trigram scores.", | |
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| "section": "Example of Scoring Fnnction", | |
| "sec_num": null | |
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| "text": "So, \"NP --~ noun noun\" will be used first to advance the \"S . NP VP\" rnle. Further, even if the parser advances both NP hypotheses, the \"S ~ NP . VP\" rnle using \"NP --~ noun noun\" will have a higher score than the \"S ~ NP . VP\" rule using \"NP ---~ 111011I'I .~", | |
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| "section": "Example of Scoring Fnnction", | |
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| "text": "The interleaved architecture implemented in .pearl provides many advantages over the traditional pipeline architecture, but it also introduces certain risks. Decisions about word and partof-speech ambiguity can be delayed nntil syntactic processing can SWe are not deliberately avoiding using all probability estimation techniques, only those backLItg-O~ teclLttiqu.eS wltich thse itLdel.)endence \u00a2~ssump-~ons that frequently provide misleading information when applied to natural language. disarnbignate them. And, using the appropriate score combina-/,ion fimctions, the scoring of ambiguous choices can direct the parser towards the most likely interpretation efficiently.", | |
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| "section": "INTERLEAVED ARCHITECTURE IN PEARL", | |
| "sec_num": null | |
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| "text": "However, with these delayed decisions comes a. vastly enlarged search space. The effectiveness of the parser depends on a majority of the theories having very low scores barred on either unlikely syntactic struct~Jres or low scoring input (such as low scores from a speech recognizer or low lexical probability). In experiments we have performed, this has been the case.", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "INTERLEAVED ARCHITECTURE IN PEARL", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Pearl is an agenda~ba~sed time-asynchronous bottom-up chart parser with Earley-type top-down prediction. The significant difference between T~earl and non-probabilistic bottom-up parsers is that instead of completely generating all grammatical interpretations of a word string, ~earl uses an agenda to order the incomplete theories in its chart to determine which theory to advance next. The agenda is sorted by the value of the theory scoring fimction described above. Instead of expanding all theories in the chart, Pearl pl~rsnes the highest-scoring incomplete theories in the chart, advancing up to N theories at each pass. However, T~earl parses without pruning. Although it is only advancing N incomplete theories at each pass, it retains the lower scoring theories in its agenda. If the higher scoring theories do not generate viable alternatives, the lower scoring theories may be used on snbseqnent passes.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The Parsing Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The parsing algorithm begins with an input word lattice, which describes the input sentence and includes possible idiom bypothese and may include alternative word hypotheses. \"q Lexical rules for /.he input word lattice are inserted into the parser's chart,. Using Earley-type prediction, a sentence (S) is predicted at the beginning of the input, and all of the theories which are predicted by that initial sentence are inserted into the chart. These incomplete theories are scored according to the context-sensitive conditional probabilities and the trigrarn part-of-speech model. The incomplete theories are tested in order by score, until N theories are advanced, m , The resulting advanced theories are scored and predicted for, and the new incomplete predicted theories are scored and added to the chart. This process continues until an complete parse tree is determined, or nnt~il the parser decides, heuristically, that it should not continue. The heuristics we used for determining that no parse can be found for an input are based on the highest, scoring incomplete theory inn the chart, the number of passes the parser hans made, and the size of the chart.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "The Parsing Algorithm", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Besides using statistical methods to guide the parser through the parsing search space, \"Pearl also performs other fimctions 0 Usi*tg alternative word hypotheses without incorporating a speech recogtfition model would not necessarily produce ttsefftd results. Given two unambigttous norms at the same position in the sentence, \"Pearl has no information with wlfich to disambiguate these words, and will invariably select thefirst one entered into the chart. The capability to process a alternate word hypotheses is inchtded to suggezt the future implementation off a speedt recognition modal i, +Pearl.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "J%Ve believe that N depends on the perplexity off the grammar used, but for the string grammar used for ottr experiments we itsed N=3. For the pttrp(yses off training, we sttgg\u00a2~l, that a higher N shottld be used in order to generate more parses.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "which are crncial to robustly processing unrestricted natliral language text and speech.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Handling Unknown Words Pearl uses a very simple probabilistic unknown word model to hypothesize categories for unknown words. When a word is fonnd which is unknown to the system's lexicon, the word is a.ssumed to be any one of the open cla~ss categories. The lexical probability given a category is the probability of that category occurring in the training corpns.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Idiom Processing and Lattice Parsing Since the parsing search space can be simplified by recognizing idion~s, Pearl allows the inpnt string to inch~de idiorrrs that. span more than one word in the sentence. This is accomplished by viewing the input sentence as a word lattice instead of a word string. Since idioo~s tend to be nnambignous with respect to part-of-speech, they are generally favored over processing the individual words that make up the idiom, since the scores of rules containing the words will tend to be lens than 1, while a syntactically appropriate~ unambiguous idiom will have a score of close to 1.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The ability to parse a sentence with mnltiple word hypotheses and word boundary hypotheses makes Pearl very nsefifi in the domain of spoken language processing. By delaying decisions about word selection but maintaining scoring information from a speech recognizer, the parser can use grammatical information in word selection without slowing the speech recognition process. Because of Pearl's interleaved architecture, one conld ea.sily incorporate scoring information from a speech recognizer into the set of scoring fl]nctions used in the parser. 'Pearl could also provide feedback to the speech recognizer abont the grarnmaticality of fragment hypotheses to glfide the recognizer's search.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Partial Parses The main advantage of chart-barred parsing over other parsing algorithms is that a chart-based parser can recognize well-formed substrings within the input string in the course of pursuing a complete parse. Pearl takes fi,ll advantage of this characteristic. Once Pearl is given the input sentence, it awaits instructions as to what type of parse should be attempted for this input. A standard parser automatically attempts to prodace a sentence (S) spanning the entire inplJt string. However, if this fails, the semantic interpreter might be able to derive some meaning from the sentence if given non-overlapping noun, verb, and prepositional phrases. If a sentence fails to parse, requests for partial parses of the input string can be made by specifying a range which the parse tree should cover and the category (NP, VP, etc.). These requests, however, must be initiated by an intelligent semantics processor which can manipulate these partial parses.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Trainability One of the major advantages of the probabilistic parsers is trainability. The conditional probabilities used by Pearl are estimated by using frequencies from a large corpus of parsed sentences. The parsed sentences must be parsed using the grammar formalism which the Pearl will use.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Assuming the grammar is not recnrsive in an unconstrained way, the parser can be trained in an unsupervised mode. This is accomplished by running the parser without the scoring flmctions, and generating many parse trees for each sentence. Previous work H has demonstrated that the correct information from nThis is art unpublished result, reportedly due to Fujisaki at IBM Japan.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "these parse trees will be reinforced, while the incorrect substructure will not. Multiple passes of re-training using frequency data from the previous pass should creme the frequency tables to converge to a stable state. This hypothesis has not yet been tested, t2", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "An alternative to completely unsupervised training is to take a parsed corpus for any domain of the same language using the same grammar, and use the frequency data from that corpus as the initial training material for the new corpus. This approach should serve only to minimize the number of nnsupervised passes required for the frequency data to converge.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Pearl's Capabilities", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In order to test Pearl's capabilities, we performed some simple tests to determine if its performance is at least consistent with the premises upon which it is bmsed. The test sentences used for this evaluation are not from the training dataon which the parser was trained. Using Pearl's context-free grammar, which is equivalent to the context-free backbone of PUNDIT'S grammar, these test sentences produced an average of 64 parses per sentence , with some sentences producing over 100 parses.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "PARSING THE VOYAGEI~ DOMAIN", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The 40 test sentences were parsed by \"Pearl and the highest scoring parse fbr each sentence was compared to the correct parse produced by PUNDIT. Of these 40 sentences, \"Pearl produced parse trees fbr 38 of them, and 35 of these parse trees were equivalent to the correct parse produced by PUNDIT, fbr an overall accuracy rate of 88%. Although precise accuracy statistics are not available ibr PUNDIT, this result is believed to be comparable to PUNDIT's perfbrmance. However, the result is achieved without the painfully hand-crafted restriction grammar associated with PUNDIT'S parser.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Overall Parsing Accuracy", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Many of the test sentences were not difficult to parse fbr existing parsers, but most had some grammatical ambiguity which would produce multiple parses. In fkct, on 2 of the 3 sentences which were incorrectly parsed, \"Pearl produced the correct parse as well, but the correct parse did not have the highest score. And both of these sentences would have been correctly processed if' semantic filtering were used on the top three parses.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Overall Parsing Accuracy", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Of the two sentences which did not parse, one used passive voice, which only occurred in one sentence in the training corpus. While the other sentence, How can I got from care sushi to Cambridge City Hospital by walking did not produce a parse for the entire word string, it could be processed using \"Pearl's partial parsing capability. By accessing the chart produced by the failed parse attempt, the parser can find a parsed sentence containing the first eleven words, and a prepositional phrase containing the final two words. This infbrmation could be used to interpret the sentence properly.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Overall Parsing Accuracy", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "12In fact, for certain grammars, the frequency tables may not converge at all, or they may converge to zero, with the grammar generating no parses for the entire corpus. This is a worst-ease scenario which we do not anticipate happening.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Overall Parsing Accuracy", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Unknown Word Part-of-speech Assignment To determine how \"Pearl handles unknown words, we randomly selected five words f~om the test sentences, [, know, ~cc, dcscriSc, removed their entries f~om the lexicon, and stalion, and tried to parse the 40 sample sentences using the simple unknown word model previously described) ~", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Overall Parsing Accuracy", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In this test, the pronoun, /, was assigned the correct part-of: speech 9 of 10 times it occurred in the test sentences. The nouns, ~ee and station, were correctly tagged 4 of 5 times. And the verbs, know and describc, were correctly tagged 3 of 3 times. accuracy is expected for unknown words in isolation, based on the accuracy of' the part-of:speech tagging model, the perfbrmance is expected to degrade for sequences of\" unknown words.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Overall Parsing Accuracy", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Accurately determining prepositional phrase attachment in general is a difficult and well-documented problem. However, based on experience with several different domains, we have ibund prepositional phrase attachment to be a domain-specific phenomenon for which training can be very helpful. For instance, in the direction-finding domain, from and to prepositional phrases generally attach to the preceding verb and not to any noun phrase. This tendency is captured in the training process for \"Pearl and is used to guide the parser to the more likely attachment with respect to the domain. This does not mean that \"Pearl will get the correct parse when the less likely attachment is correct; in fact, \"Pearl will invariably get this case wrong. However, based on the premise that this is the less likely attachment, this will produce more correct analyses than incorrect. And, using a more sophisticated statistical model which uses more contextual infbrmation, this perfbrmance can likely be improved. \"Pearl's perfbrmance on prepositional phrase attachment was very high (54/55 or 98.2% correct). The reason the accuracy rate is so high is that the direction-finding domain is very consistent in its use of individual prepositions. The accuracy rate is not expected to be as high in less consistent domains, although we expect it to be significantly higher than chance.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Prepositional Phrase Attachment", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "One claim of \"Pearl, and of probabilistic parsers in general, is that probabilities can help guide a parser through the immense search space produced by ambiguous grammars. Since, without probabilisties, the test sentences produced an average of 64 parses per sentence, \"Pearl unquestionably has reduced the space of possibilities by only producing [3] , and two versions of the CFG with CSP model, one using the geometric mean of raw theory scores and the other using the product of\" these raw scores. Using a simple probabilistic CFG model, the parser produced a much lower accuracy rate (35%). The parentM conditioning brought this rate up to 50%, and the trigram conditioning brought this level up to 88%. The search space for CFG with CSP was 4 to 5 times lower than the simple probabilistic CFG.", | |
| "cite_spans": [ | |
| { | |
| "start": 349, | |
| "end": 352, | |
| "text": "[3]", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Search Space Reduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The \"Pearl parser takes advantage of domain-dependent information to select the most appropriate interpretation of an input. However, the statistical measure used to disambiguate these interpretations is sensitive to certain attributes of' the grammatical ibrmalism used, as well as to the part-of-speech categories used to label lexical entries. All of the experiments perfbrmed on \"Pearl thus far have been using one grammar, one part-of-speech tag set, and one domain (because of availability constraints). Future experiments are planned to e~xluate \"Pearl's perfbrmance on different domains, as well as on a general corpus of English, and on different grammars, including a grammar derived fl'om a manually parsed corpus.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "FUTURE WORK", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Specifically, we plan to retrain \"Pearl on a corpus of terroristrelated messages fl'om the Message Understanding Conference (MUC). Using this material, we will attempt two very different experiments. The first experiment will be similar to the one performed on the Voyager data. Using a corpus of correctly parsed MUC sentences fl'om SRI's Tacitus system, we will derive a context-f~ee grammar and extract training statistics ibr \"Pearl's models. Since the MUC sentences exhibit many more difficulties than Voyager, including 50 word sentences, punctuation, no sentence markers, and typographical errors, we expect \"Pearl to require significant re-engineering to handle this experiment.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "FUTURE WORK", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "'The second experiment on the MUC corpus involves extracting a grammar and training statistics from a hand-parsed corpus. When the University of\" Pennsylvania's Treebank project [2] makes a hand-parsed version of the MUG training material a~ilable to the DARPA community, we will extract a context-f~ee grammar from these parse trees, and retrain ~earl on this material. This experiment is even more interesting because, if successful, it will show that ~Oearl provides an alternative to the hand-pruning of grammars to cover specific domains. If a hand-parsed corpus can provide a covering grammar which can be used to accurately parse a particular domain, porting natural language applications to new domains will be greatly facilitated.", | |
| "cite_spans": [ | |
| { | |
| "start": 178, | |
| "end": 181, | |
| "text": "[2]", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "FUTURE WORK", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The probabilistic parser which we have described provides a platibrm for exploiting the useful ini:brmation made available by statistical models in a manner which is consistent with existing grammar fbrmalisms and parser designs. \"Pearl can be trained to use any context-f~ee grammar, accompanied by the appropriate training material. And, the parsing algorithm is very similar to a standard bottom-up algorithm, with the exception of using theory scores to order the search.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CONCLUSION", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In experiments on the Voyager direction-finding domain, ~earl, using only a context-i~ee grammar and statistical models, perfbrmed at least as well as PUNDIT'S parser, which includes handgenerated restrictions. In the ihture, we hope to demonstrate similar peribrmance on more difficult domains and using manually parsed corpora.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "CONCLUSION", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
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| "FIGREF0": { | |
| "type_str": "figure", | |
| "num": null, | |
| "text": "Stochastic techniques combined with traditional hnguistic theories can (and indeed must) provide a solution to the natural language understanding problem. *This work was partially supportcd by DARPA grant No. Nt1014-85-K0018, ONR contract No. Ntltltl14-89-O~0171 by DARPA and AIrOSR jointly ttndcr grant No. AFOSR-90-Ut166, and by ARO grant No. DAAL 03-89-Ctlt131 PRL SpccJal thanks to Carl Wcir and Lynettc Hirschman at Unisys for thcir valucd input, guidancc and support.", | |
| "uris": null | |
| }, | |
| "FIGREF1": { | |
| "type_str": "figure", | |
| "num": null, | |
| "text": "As an example of how the conditionalprobability-based scoring fimction handles ambiguity, consider the sentence Fruit flies like a banana. in the domain of insect studies. Lexica.I probabilities should indicate that the word \"flies\" is more likely to be a plural noun than a tensed verb. This information is incorporated in the trigram scores. However, when the interpretation S-+. NPVP is proposed, two possible NPs will be parsed, NP --~ noun (frnit) and NP ~ noun nmm (fruit file.6).", | |
| "uris": null | |
| }, | |
| "FIGREF2": { | |
| "type_str": "figure", | |
| "num": null, | |
| "text": "3 parses per sentence while maintaining nThe unknown word model used in this test was augmented to include dosed class categories as well as open class, since the words removed from the lexicon may have included (in fact did include) dosed dass words.", | |
| "uris": null | |
| }, | |
| "FIGREF3": { | |
| "type_str": "figure", | |
| "num": null, | |
| "text": "Figure 2: Preposition", | |
| "uris": null | |
| } | |
| } | |
| } | |
| } |