Coreference resolution using competition learning approach
Published 1 January 2003Open access
Xiaofeng Yang, Guodong Zhou, Jian Su, Chew Lim Tan
Citations139
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The experimental results show that the proposed competition learning approach to coreference resolution can outperform those based on the single-candidate model and applies a candidate filter to reduce the computational cost and data noises during training and resolution.
Abstract
In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the single-candidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected. Furthermore, our approach applies a candidate filter to reduce the computational cost and data noises during training and resolution. The experimental results on MUC-6 and MUC-7 data set show that our approach can outperform those based on the single-candidate model.
Keywords
Computer Science
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Elsevier eBooksFast Effective Rule Induction
3,767 Citations1995William W. Cohen
This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples.
Computational LinguisticsA Machine Learning Approach to Coreference Resolution of Noun Phrases
998 Citations2001Wee Meng Soon, Hwee Tou Ng +1 more
The learning approach to coreference resolution of noun phrases in unrestricted text is presented, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches.
Named entity recognition using an HMM-based chunk tagger
718 Citations2001Guodong Zhou, Jian Su
A Hidden Markov Model and an HMM-based chunk tagger is proposed, from which a named entity (NE) recognition system is built to recognize and classify names, times and numerical quantities, and the NER problem can be resolved effectively.
Improving machine learning approaches to coreference resolution
709 Citations2001Vincent Ng, Claire Cardie
A noun phrase coreference system that extends the work of Soon et al. (2001) and produces the best results to date on the M UC-6 and MUC-7 coreference resolution data sets --- F-measures of 70.4 and 63.4, respectively.
A model-theoretic coreference scoring scheme
682 Citations1995Marc Vilain, John D. Burger +3 more
This note describes a scoring scheme for the coreference task in MUC6 that improves on the original approach by grounding the scoring scheme in terms of a model; producing more intuitive recall and precision scores; and not requiring explicit computation of the transitive closure of coreference.
A centering approach to pronouns
540 Citations1987Susan E. Brennan, Marilyn Walker Friedman +1 more
A formalization of the centering approach to modeling attentional structure in discourse is presented and used as the basis for an algorithm to track discourse context and bind pronouns and proposes an extension to these states of continuing, retaining and shifting.
Robust pronoun resolution with limited knowledge
284 Citations1998Ruslan Mitkov
This paper presents a robust, knowledge-poor approach to resolving pronouns in technical manuals, which operates on texts pre-processed by a part-of-speech tagger, and can be successfully adapted for other languages with minimum modifications.
Evaluating automated and manual acquisition of anaphora resolution strategies
176 Citations1995Chinatsu Aone, Scott Bennett
This work uses Japanese newspaper articles tagged with discourse information as training examples for a machine learning algorithm which employs the C4.5 decision tree algorithm by Quinlan (Quinlan, 1993) to build an automatically trainable anaphora resolution system.
Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution
158 Citations2002Vincent Ng, Claire Cardie
A supervised learning approach to identification of anaphoric and non-anaphoric noun phrases is presented and it is shown how such information can be incorporated into a coreference resolution system that outperforms the best M UC-6 and MUC-7 coreferenceresolution systems on the corresponding MUC coreference data sets.
Computational LinguisticsA Corpus-Based Evaluation of Centering and Pronoun Resolution
101 Citations2001Joel Tetreault
A centering algorithm (Left-Right Centering) is introduced that adheres to the constraints and rules of centering theory and is an alternative to Brennan, Friedman, and Pollard's (1987) algorithm.
Proceedings of the 17th international conference on Computational linguistics -Robust pronoun resolution with limited knowledge
99 Citations1998Ruslan Mitkov
ANAPHORA RESOLUTION: THE STATE OF THE ART
98 Citations2007Ruslan Mitkov
This fusion in vitro of chromaffin granules and target membrane vesicles and a Ca2(+)-dependent phospholipase A2 (PLA2) is reconstituted, suggesting that during a second process arachidonic acid may be processed to the terminal fusogen.
Never look back
76 Citations1998Michael Strube
A model for determining the hearer's attentional state which depends solely on a list of salient discourse entities (S-list), which is the basis for an algorithm which operates incrementally, word by word.
Corpus-based identification of non-anaphoric noun phrases
66 Citations1999David L. Bean, Ellen Riloff
A corpus-based algorithm is developed for automatically identifying definite noun phrases that are non-anaphoric, which has the potential to improve the efficiency and accuracy of coreference resolution systems.
Error-driven HMM-based chunk tagger with context-dependent lexicon
30 Citations2000Guodong Zhou, Jian Su
This paper proposes a new error-driven HMM-based text chunk tagger with context-dependent lexicon which uses a new Hidden Markov Modelling approach which incorporates more contextual information into a lexical entry.
Scholarworks (University of Massachusetts Amherst)A trainable approach to coreference resolution for information extraction
26 Citations1996Joseph F. McCarthy, Wendy G. Lehnert
This dissertation presents a new system, named R scESOLVE, that uses machine learning techniques to determine when two phrases in a test co-refer, i.e., refer to the same thing, in a natural language processing task known as information extraction.