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Search Results: 1 - 10 of 2499 matches for " Aravind Joshi "
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Coordination in Tree Adjoining Grammars: Formalization and Implementation
Anoop Sarkar,Aravind Joshi
Computer Science , 1996,
Abstract: In this paper we show that an account for coordination can be constructed using the derivation structures in a lexicalized Tree Adjoining Grammar (LTAG). We present a notion of derivation in LTAGs that preserves the notion of fixed constituency in the LTAG lexicon while providing the flexibility needed for coordination phenomena. We also discuss the construction of a practical parser for LTAGs that can handle coordination including cases of non-constituent coordination.
Complexity of Scrambling: A New Twist to the Competence - Performance Distinction
Aravind K Joshi
Computer Science , 1994,
Abstract: In this paper we discuss the following issue: How do we decide whether a certain property of language is a competence property or a performance property? Our claim is that the answer to this question is not given a-priori. The answer depends on the formal devices (formal grammars and machines) available to us for describing language. We discuss this issue in the context of the complexity of processing of center embedding (of relative clauses in English) and scrambling (in German, for example) from arbitrary depths of embedding.
A Formal Look at Dependency Grammars and Phrase-Structure Grammars, with Special Consideration of Word-Order Phenomena
Owen Rambow,Aravind Joshi
Computer Science , 1994,
Abstract: The central role of the lexicon in Meaning-Text Theory (MTT) and other dependency-based linguistic theories cannot be replicated in linguistic theories based on context-free grammars (CFGs). We describe Tree Adjoining Grammar (TAG) as a system that arises naturally in the process of lexicalizing CFGs. A TAG grammar can therefore be compared directly to an Meaning-Text Model (MTM). We illustrate this point by discussing the computational complexity of certain non-projective constructions, and suggest a way of incorporating locality of word-order definitions into the Surface-Syntactic Component of MTT.
Some Novel Applications of Explanation-Based Learning to Parsing Lexicalized Tree-Adjoining Grammars
B. Srinivas,Aravind Joshi
Computer Science , 1995,
Abstract: In this paper we present some novel applications of Explanation-Based Learning (EBL) technique to parsing Lexicalized Tree-Adjoining grammars. The novel aspects are (a) immediate generalization of parses in the training set, (b) generalization over recursive structures and (c) representation of generalized parses as Finite State Transducers. A highly impoverished parser called a ``stapler'' has also been introduced. We present experimental results using EBL for different corpora and architectures to show the effectiveness of our approach.
Disambiguation of Super Parts of Speech (or Supertags): Almost Parsing
Aravind K. Joshi,B. Srinivas
Computer Science , 1994,
Abstract: In a lexicalized grammar formalism such as Lexicalized Tree-Adjoining Grammar (LTAG), each lexical item is associated with at least one elementary structure (supertag) that localizes syntactic and semantic dependencies. Thus a parser for a lexicalized grammar must search a large set of supertags to choose the right ones to combine for the parse of the sentence. We present techniques for disambiguating supertags using local information such as lexical preference and local lexical dependencies. The similarity between LTAG and Dependency grammars is exploited in the dependency model of supertag disambiguation. The performance results for various models of supertag disambiguation such as unigram, trigram and dependency-based models are presented.
A Processing Model for Free Word Order Languages
Owen Rambow,Aravind K. Joshi
Computer Science , 1995,
Abstract: Like many verb-final languages, Germn displays considerable word-order freedom: there is no syntactic constraint on the ordering of the nominal arguments of a verb, as long as the verb remains in final position. This effect is referred to as ``scrambling'', and is interpreted in transformational frameworks as leftward movement of the arguments. Furthermore, arguments from an embedded clause may move out of their clause; this effect is referred to as ``long-distance scrambling''. While scrambling has recently received considerable attention in the syntactic literature, the status of long-distance scrambling has only rarely been addressed. The reason for this is the problematic status of the data: not only is long-distance scrambling highly dependent on pragmatic context, it also is strongly subject to degradation due to processing constraints. As in the case of center-embedding, it is not immediately clear whether to assume that observed unacceptability of highly complex sentences is due to grammatical restrictions, or whether we should assume that the competence grammar does not place any restrictions on scrambling (and that, therefore, all such sentences are in fact grammatical), and the unacceptability of some (or most) of the grammatically possible word orders is due to processing limitations. In this paper, we will argue for the second view by presenting a processing model for German.
Anchoring a Lexicalized Tree-Adjoining Grammar for Discourse
Bonnie Lynn Webber,Aravind K. Joshi
Computer Science , 1998,
Abstract: We here explore a ``fully'' lexicalized Tree-Adjoining Grammar for discourse that takes the basic elements of a (monologic) discourse to be not simply clauses, but larger structures that are anchored on variously realized discourse cues. This link with intra-sentential grammar suggests an account for different patterns of discourse cues, while the different structures and operations suggest three separate sources for elements of discourse meaning: (1) a compositional semantics tied to the basic trees and operations; (2) a presuppositional semantics carried by cue phrases that freely adjoin to trees; and (3) general inference, that draws additional, defeasible conclusions that flesh out what is conveyed compositionally.
The biomedical discourse relation bank
Rashmi Prasad, Susan McRoy, Nadya Frid, Aravind Joshi, Hong Yu
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-188
Abstract: We have developed the Biomedical Discourse Relation Bank (BioDRB), in which we have annotated explicit and implicit discourse relations in 24 open-access full-text biomedical articles from the GENIA corpus. Guidelines for the annotation were adapted from the Penn Discourse TreeBank (PDTB), which has discourse relations annotated over open-domain news articles. We introduced new conventions and modifications to the sense classification. We report reliable inter-annotator agreement of over 80% for all sub-tasks. Experiments for identifying the sense of explicit discourse connectives show the connective itself as a highly reliable indicator for coarse sense classification (accuracy 90.9% and F1 score 0.89). These results are comparable to results obtained with the same classifier on the PDTB data. With more refined sense classification, there is degradation in performance (accuracy 69.2% and F1 score 0.28), mainly due to sparsity in the data. The size of the corpus was found to be sufficient for identifying the sense of explicit connectives, with classifier performance stabilizing at about 1900 training instances. Finally, the classifier performs poorly when trained on PDTB and tested on BioDRB (accuracy 54.5% and F1 score 0.57).Our work shows that discourse relations can be reliably annotated in biomedical text. Coarse sense disambiguation of explicit connectives can be done with high reliability by using just the connective as a feature, but more refined sense classification requires either richer features or more annotated data. The poor performance of a classifier trained in the open domain and tested in the biomedical domain suggests significant differences in the semantic usage of connectives across these domains, and provides robust evidence for a biomedical sublanguage for discourse and the need to develop a specialized biomedical discourse annotated corpus. The results of our cross-domain experiments are consistent with related work on identifying connectives
Attribution and its annotation in the Penn Discourse TreeBank
Rashmi Prasad,Nikhil Dinesh,Alan Lee,Aravind Joshi
Traitement Automatique des Langues , 2007,
Abstract: In this paper, we describe an annotation scheme for the attribution of abstract objects (propositions, facts, and eventualities) associated with discourse relations and their arguments annotated in the Penn Discourse TreeBank. The scheme aims to capture both the source and degrees of factuality of the abstract objects through the annotation of text spans signalling the attribution, and of features recording the source, type, scopal polarity, and determinacy of attribution.
Korean to English Translation Using Synchronous TAGs
Dania Egedi,Martha Palmer,Hyun S. Park,Aravind K. Joshi
Computer Science , 1994,
Abstract: It is often argued that accurate machine translation requires reference to contextual knowledge for the correct treatment of linguistic phenomena such as dropped arguments and accurate lexical selection. One of the historical arguments in favor of the interlingua approach has been that, since it revolves around a deep semantic representation, it is better able to handle the types of linguistic phenomena that are seen as requiring a knowledge-based approach. In this paper we present an alternative approach, exemplified by a prototype system for machine translation of English and Korean which is implemented in Synchronous TAGs. This approach is essentially transfer based, and uses semantic feature unification for accurate lexical selection of polysemous verbs. The same semantic features, when combined with a discourse model which stores previously mentioned entities, can also be used for the recovery of topicalized arguments. In this paper we concentrate on the translation of Korean to English.
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