BibSonomyburst/concept/tag/statistic_nlpBibSonomy RSS feed for /concept/tag/statistic_nlp2012-02-16T21:24:42+01:00http://www.bibsonomy.org/bibtex/2ecb48cd1d1818a0015f7128de2257088/diego_madiego_ma2007-12-14T02:45:54+01:00parsers statistic_nlp <span class="authorEditorList"><a href="/author/Roark">Brian Roark</a>, and <a href="/author/Johnson">Mark Johnson</a> </span><em>Proc. ACL&#039;99, </em><em>page 421-428. </em>(<em>1999</em>)<em>http://arXiv.org/abs/cs/0008017 . </em>Fri Dec 14 02:45:54 CET 2007Proc. ACL'99\myurl{http://arXiv.org/abs/cs/0008017}421-428Efficient Probabilistic Top-down and Left-corner Parsing1999parsers statistic_nlp This paper examines efficient predictive broad-coverage parsing without dynamic programming. In contrast to bottom-up methods, depth-first top-down parsing produces partial parses that are fully connected trees spanning the entire left context, from which any kind of non-local dependency or partial semantic interpretation can in principle be read. We contrast two predictive parsing approaches, top-down and left-corner parsing, and find both to be viable. In addition, we find that enhancement with non-local information not only improves parser accuracy, but also substantially improves the search efficiency.http://www.bibsonomy.org/bibtex/22fad675aa6ae88082af2507c16d54343/diego_madiego_ma2007-12-14T02:42:52+01:00statistic_nlp <span class="authorEditorList"><a href="/author/Manning">Christopher D. Manning</a>, and <a href="/author/Schütze">Hinrich Schütze</a> </span><em>MIT Press, </em><em>Cambridge, MA, </em>(<em>1999</em>)Fri Dec 14 02:42:52 CET 2007Cambridge, MAFoundations of Statistical Natural Language Processing1999statistic_nlp http://www.bibsonomy.org/bibtex/2286bd5351488a152431d27dccdea1b7c/diego_madiego_ma2007-12-14T02:41:59+01:00statistic_nlp <span class="authorEditorList"><a href="/author/Lapata">Maria Lapata</a> </span><em>Proceedings NAACL 2001, </em><em>Pittsburgh, </em>(<em>2001</em>)Fri Dec 14 02:41:59 CET 2007PittsburghProceedings NAACL 2001A Corpus-based Account of Regular Polysemy: The Case of Context-sensitive Adjectives2001statistic_nlp In this paper we investigate polysemous adjectives whose meaning varies depending on the nouns they modify (e.g., fast ). We acquire the meanings of these adjectives from a large corpus and propose a probabilistic model which provides a ranking on the set of possible interpretations. We identify lexical semantic information automatically by exploiting the consistent correspondences between surface syntactic cues and lexical meaning. We evaluate our results against paraphrase judgments elicited experimentally from humans and show that the model's ranking of meanings correlates reliably with human intuitions: meanings that are found highly probable by the model are also rated as plausible by the subjects.http://www.bibsonomy.org/bibtex/2ecd94c145e7e59310fc267ce90b70e73/diego_madiego_ma2007-12-14T02:37:34+01:00parsers statistic_nlp <span class="authorEditorList"><a href="/author/Collins">Michael John Collins</a> </span><em>Proc. ACL, </em><em>Santa Cruz, </em>(<em>1996</em>)Fri Dec 14 02:37:34 CET 2007Proc. ACLA New Statistical Parser Based on Bigram Lexical Dependencies1996parsers statistic_nlp http://www.bibsonomy.org/bibtex/25c58ec5cb110920272d7de02dd45f15d/diego_madiego_ma2007-12-14T02:36:34+01:00statistic_nlp <span class="authorEditorList"><a href="/author/Brill">Eric Brill</a> </span><em>University of Pennsylvania, </em>(<em>1993</em>)Fri Dec 14 02:36:34 CET 2007A Corpus-Based Approach to Language Learning1993statistic_nlp