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62
TransformationBased ErrorDriven Learning and Natural Language Processing: A Case Study in PartofSpeech Tagging
 Computational Linguistics
, 1995
"... this paper, we will describe a simple rulebased approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learni ..."
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Cited by 916 (7 self)
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this paper, we will describe a simple rulebased approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learning method applied to part of speech tagging
Insideoutside reestimation from partially bracketed corpora
 In Proceedings of the 30th Annual Meeting of the ACL
, 1992
"... The insideoutside algorithm for inferring the parameters of a stochastic contextfree grammar is extended to take advantage of constituent information (constituent bracketing) in a partially parsed corpus. Experiments on formal and natural language parsed corpora show that the new algorithm can ach ..."
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Cited by 305 (3 self)
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The insideoutside algorithm for inferring the parameters of a stochastic contextfree grammar is extended to take advantage of constituent information (constituent bracketing) in a partially parsed corpus. Experiments on formal and natural language parsed corpora show that the new algorithm can achieve faster convergence and better modeling of hierarchical structure than the original one. In particular, over 90 % test set bracketing accuracy was achieved for grammars inferred by our algorithm from a training set of handparsed partofspeech strings for sentences in the Air Travel Information System spoken language corpus. Finally, the new algorithm has better time complexity than the original one when sufficient bracketing is provided. 1
An Efficient Probabilistic ContextFree Parsing Algorithm that Computes Prefix Probabilities
 Computational Linguistics
, 2002
"... this article can compute solutions to all four of these problems in a single flamework, with a number of additional advantages over previously presented isolated solutions ..."
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Cited by 223 (5 self)
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this article can compute solutions to all four of these problems in a single flamework, with a number of additional advantages over previously presented isolated solutions
A Probabilistic Model of Lexical and Syntactic Access and Disambiguation
 COGNITIVE SCIENCE
, 1995
"... The problems of access  retrieving linguistic structure from some mental grammar  and disambiguation  choosing among these structures to correctly parse ambiguous linguistic input  are fundamental to language understanding. The literature abounds with psychological results on lexical access, ..."
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Cited by 203 (11 self)
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The problems of access  retrieving linguistic structure from some mental grammar  and disambiguation  choosing among these structures to correctly parse ambiguous linguistic input  are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of gardenpath sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. For example psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This paper presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Lowranked constructions and interpretations are pruned through beamsearch; this pruning accounts, among other things, for the gardenpath effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilisticallyenriched grammars and interpreters as models of human knowledge of and processing of language.
Using statistics in lexical analysis
 Lexical Acquisition: Exploiting OnLine Resources to Build a Lexicon
, 1991
"... The computational tools available for studying machinereadable corpora are at present still rather primitive. In the more advanced lexicographic organizations, there are concordancing programs (see figure below), which are basically KWIC (key word in context (Aho et al., 1988, p. 122), (Salton, 198 ..."
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Cited by 171 (3 self)
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The computational tools available for studying machinereadable corpora are at present still rather primitive. In the more advanced lexicographic organizations, there are concordancing programs (see figure below), which are basically KWIC (key word in context (Aho et al., 1988, p. 122), (Salton, 1989, p. 384)) indexes with additional features such as the ability to extend the context, sort leftwards as well as
Supertagging: An Approach to Almost Parsing
 Computational Linguistics
, 1999
"... this paper, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated wit ..."
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Cited by 167 (23 self)
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this paper, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (Supertags) that impose complex constraints in a local context. The supertags are designed such that only those elements on which the lexical item imposes constraints appear within a given supertag. Further, each lexical item is associated with as many supertags as the number of different syntactic contexts in which the lexical item can appear. This makes the number of different descriptions for each lexical item much larger, than when the descriptions are less complex; thus increasing the local ambiguity for a parser. But this local ambiguity can be resolved by using statistical distributions of supertag cooccurrences collected from a corpus of parses. We have explored these ideas in the context of Lexicalized TreeAdjoining Grammar (LTAG) framework. The supertags in LTAG combine both phrase structure information and dependency information in a single representation. Supertag disambiguation results in a representation that is effectively a parse (almost parse), and the parser needs `only' combine the individual supertags. This method of parsing can also be used to parse sentence fragments such as in spoken utterances where the disambiguated supertag sequence may not combine into a single structure. 1 Introduction In this paper, we present a robust parsing approach called supertagging that integrates the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. The idea underlying the approach is that the ...
Parameter learning of logic programs for symbolicstatistical modeling
 Journal of Artificial Intelligence Research
, 2001
"... We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distributio ..."
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Cited by 124 (21 self)
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We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, thatrunsfora class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the BaumWelch algorithm for HMMs, the InsideOutside algorithm for PCFGs, and the one for singly connected Bayesian networks that have beendeveloped independently in each research eld. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can signi cantly outperform the InsideOutside algorithm. 1.
PartofSpeech Tagging and Partial Parsing
 CorpusBased Methods in Language and Speech
, 1996
"... m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of t ..."
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Cited by 110 (0 self)
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m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but nonzero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of handconstructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then handedited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on
Designing Statistical Language Learners: Experiments on Noun Compounds
, 1995
"... Statistical language learning research takes the view that many traditional natural language processing tasks can be solved by training probabilistic models of language on a sufficient volume of training data. The design of statistical language learners therefore involves answering two questions: (i ..."
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Cited by 94 (0 self)
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Statistical language learning research takes the view that many traditional natural language processing tasks can be solved by training probabilistic models of language on a sufficient volume of training data. The design of statistical language learners therefore involves answering two questions: (i) Which of the multitude of possible language models will most accurately reflect the properties necessary to a given task? (ii) What will constitute a sufficient volume of training data? Regarding the first question, though a variety of successful models have been discovered, the space of possible designs remains largely unexplored. Regarding the second, exploration of the design space has so far proceeded without an adequate answer. The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: it identifies a new class of designs by providing a novel theory of statistical natural language processing, and it presents the foundations for a predictive theory of data requirements to assist in future design explorations. The first of these contributions is called the meaning distributions theory. This theory