Statistical dependency parsing

Xem 1-19 trên 19 kết quả Statistical dependency parsing
  • In this paper, we present a novel approach which incorporates the web-derived selectional preferences to improve statistical dependency parsing. Conventional selectional preference learning methods have usually focused on word-to-class relations, e.g., a verb selects as its subject a given nominal class.

    pdf10p hongdo_1 12-04-2013 26 3   Download

  • Statistical systems with high accuracy are very useful in real-world applications. If these systems can capture basic linguistic information, then the usefulness of these statistical systems improve a lot. This paper is an attempt at incorporating linguistic constraints in statistical dependency parsing. We consider a simple linguistic constraint that a verb should not have multiple subjects/objects as its children in the dependency tree.

    pdf6p hongdo_1 12-04-2013 27 2   Download

  • This paper presents results from the first statistical dependency parser for Turkish. Turkish is a free-constituent order language with complex agglutinative inflectional and derivational morphology and presents interesting challenges for statistical parsing, as in general, dependency relations are between “portions” of words – called inflectional groups. We have explored statistical models that use different representational units for parsing.

    pdf8p bunthai_1 06-05-2013 41 2   Download

  • Pure statistical parsing systems achieves high in-domain accuracy but performs poorly out-domain. In this paper, we propose two different approaches to produce syntactic dependency structures using a large-scale hand-crafted HPSG grammar. The dependency backbone of an HPSG analysis is used to provide general linguistic insights which, when combined with state-of-the-art statistical dependency parsing models, achieves performance improvements on out-domain tests.

    pdf9p hongphan_1 14-04-2013 31 2   Download

  • This paper proposes an approach to enhance dependency parsing in a language by using a translated treebank from another language. A simple statistical machine translation method, word-by-word decoding, where not a parallel corpus but a bilingual lexicon is necessary, is adopted for the treebank translation. Using an ensemble method, the key information extracted from word pairs with dependency relations in the translated text is effectively integrated into the parser for the target language.

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  • Efficiency is a prime concern in syntactic MT decoding, yet significant developments in statistical parsing with respect to asymptotic efficiency haven’t yet been explored in MT. Recently, McDonald et al. (2005b) formalized dependency parsing as a maximum spanning tree (MST) problem, which can be solved in quadratic time relative to the length of the sentence. They show that MST parsing is almost as accurate as cubic-time dependency parsing in the case of English, and that it is more accurate with free word order languages. ...

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  • Transforming syntactic representations in order to improve parsing accuracy has been exploited successfully in statistical parsing systems using constituency-based representations. In this paper, we show that similar transformations can give substantial improvements also in data-driven dependency parsing. Experiments on the Prague Dependency Treebank show that systematic transformations of coordinate structures and verb groups result in a 10% error reduction for a deterministic data-driven dependency parser.

    pdf8p hongvang_1 16-04-2013 38 2   Download

  • In this paper, we present a method that improves Japanese dependency parsing by using large-scale statistical information. It takes into account two kinds of information not considered in previous statistical (machine learning based) parsing methods: information about dependency relations among the case elements of a verb, and information about co-occurrence relations between a verb and its case element.

    pdf8p hongvang_1 16-04-2013 25 1   Download

  • We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model.

    pdf9p bunbo_1 17-04-2013 30 2   Download

  • In this paper, we present an approach as pre-processing step based on a dependency parser in phrase-based statistical machine translation (SMT) to learn automatic and manual reordering rules from English to Vietnamese. The dependency parse trees and transformation rules are used to reorder the source sentences and applied for systems translating from English to Vietnamese. We evaluated our approach on English-Vietnamese machine translation tasks, and showed that it outperforms the baseline phrase-based SMT system.

    pdf14p truongtien_09 10-04-2018 19 2   Download

  • This paper considers statistical parsing of Czech, which differs radically from English in at least two respects: (1) it is a highly inflected language, and (2) it has relatively free word order. These differences are likely to pose new problems for techniques that have been developed on English. We describe our experience in building on the parsing model of (Collins 97). Our final results - 80% dependency accuracy - represent good progress towards the 91% accuracy of the parser on English (Wall Street Journal) text. ...

    pdf8p bunrieu_1 18-04-2013 23 3   Download

  • Sentence compression is a task of creating a short grammatical sentence by removing extraneous words or phrases from an original sentence while preserving its meaning. Existing methods learn statistics on trimming context-free grammar (CFG) rules. However, these methods sometimes eliminate the original meaning by incorrectly removing important parts of sentences, because trimming probabilities only depend on parents’ and daughters’ non-terminals in applied CFG rules. We apply a maximum entropy model to the above method. ...

    pdf8p hongvang_1 16-04-2013 34 2   Download

  • Most of the work on treebank-based statistical parsing exclusively uses the WallStreet-Journal part of the Penn treebank for evaluation purposes. Due to the presence of this quasi-standard, the question of to which degree parsing results depend on the properties of treebanks was often ignored. In this paper, we use two similar German treebanks, T¨ Ba-D/Z and NeGra, u and investigate the role that different annotation decisions play for parsing.

    pdf6p hongvang_1 16-04-2013 30 2   Download

  • We present the first application of the head-driven statistical parsing model of Collins (1999) as a simultaneous language model and parser for largevocabulary speech recognition. The model is adapted to an online left to right chart-parser for word lattices, integrating acoustic, n-gram, and parser probabilities. The parser uses structural and lexical dependencies not considered by ngram models, conditioning recognition on more linguistically-grounded relationships.

    pdf8p bunbo_1 17-04-2013 24 1   Download

  • We present a linguistically-motivated algorithm for reconstructing nonlocal dependency in broad-coverage context-free parse trees derived from treebanks. We use an algorithm based on loglinear classifiers to augment and reshape context-free trees so as to reintroduce underlying nonlocal dependencies lost in the context-free approximation. We find that our algorithm compares favorably with prior work on English using an existing evaluation metric, and also introduce and argue for a new dependency-based evaluation metric. ...

    pdf8p bunbo_1 17-04-2013 35 1   Download

  • This paper compares a number of generative probability models for a widecoverage Combinatory Categorial Grammar (CCG) parser. These models are trained and tested on a corpus obtained by translating the Penn Treebank trees into CCG normal-form derivations. According to an evaluation of unlabeled word-word dependencies, our best model achieves a performance of 89.9%, comparable to the figures given by Collins (1999) for a linguistically less expressive grammar. In contrast to Gildea (2001), we find a significant improvement from modeling wordword dependencies. ...

    pdf8p bunmoc_1 20-04-2013 28 1   Download

  • This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method performs at least as well as S P A T T E R (Magerman 95; Jelinek et al. 94), which has the best published results for a statistical parser on this task.

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  • The work reported here has largely involved problems with parsing Italian. One of the typical features of Italian is a lower degree of word order rigidity in sentences. For instance, "Paolo ama Maria" (Paolo loves Maria) may be rewritten without any significant difference in meaning (leaving aside questions of context and pragmatics) in any the six possible permutations: Paolo ama Maria, Paolo Maria ama, Maria ama Paolo, Maria Paolo ama, ama Paolo Maria, ama Maria Paolo.

    pdf5p buncha_1 08-05-2013 19 1   Download

  • The precise formulation of derivation for treeadjoining grammars has important ramifications for a wide variety of uses of the formalism, from syntactic analysis to semantic interpretation and statistical language modeling. We argue that the definition of tree-adjoining derivation must be reformulated in order to manifest the proper linguistic dependencies in derivations. The particular proposal is both precisely characterizable, through a compilation to linear indexed grammars, and computationally operational, by virtue of an efficient algorithm for recognition and parsing. ...

    pdf10p bunmoc_1 20-04-2013 34 2   Download



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