Computational Linguistics

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A Global Joint Model for Semantic Role Labeling
Computational Linguistics - Tập 34 Số 2 - Trang 161-191 - 2008
Toutanova, Kristina, Haghighi, Aria, Manning, Christopher D.
Automatic Summarization of Open-Domain Multiparty Dialogues in Diverse Genres
Computational Linguistics - Tập 28 Số 4 - Trang 447-485 - 2002
Klaus Zechner
Automatic summarization of open-domain spoken dialogues is a relatively new research area. This article introduces the task and the challenges involved and motivates and presents an approach for obtaining automatic-extract summaries for human transcripts of multiparty dialogues of four different genres, without any restriction on domain. We address the following issues, which are intrinsic to spok... hiện toàn bộ
Generating Indicative-Informative Summaries with SumUM
Computational Linguistics - Tập 28 Số 4 - Trang 497-526 - 2002
Horacio Saggion, Guy Lapalme
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a firs... hiện toàn bộ
Introduction to the Special Issue on Summarization
Computational Linguistics - Tập 28 Số 4 - Trang 399-408 - 2002
Dragomir Radev, Eduard Hovy, Kathleen McKeown
Automatic Evaluation of Information Ordering: Kendall's Tau
Computational Linguistics - Tập 32 Số 4 - Trang 471-484 - 2006
Mirella Lapata
This article considers the automatic evaluation of information ordering, a task underlying many text-based applications such as concept-to-text generation and multidocument summarization. We propose an evaluation method based on Kendall's τ, a metric of rank correlation. The method is inexpensive, robust, and representation independent. We show that Kendall's τ correlates reliably with human ratin... hiện toàn bộ
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics - Tập 32 Số 1 - Trang 13-47 - 2006
Alexander Budanitsky, Graeme Hirst
The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those pro... hiện toàn bộ
Computational Linguistics and Deep Learning
Computational Linguistics - Tập 41 Số 4 - Trang 701-707 - 2015
Manning, Christopher D.
Universal Dependencies
Computational Linguistics - Tập 47 Số 2 - Trang 255-308 - 2021
de Marneffe, Marie-Catherine, Manning, Christopher D., Nivre, Joakim, Zeman, Daniel
Tổng số: 8   
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