Open AccessProceedings Article
Single and multi-objective optimization for feature selection in anaphora resolution
Sriparna Saha,Asif Ekbal,Olga Uryupina,Massimo Poesio +3 more
- 01 Nov 2011
- pp 93-101
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TL;DR: This paper shows that optimize models according to multiple metrics simultaneously may result in better results with respect to each individual metric than optimizing according to that metric only, and that this is possible to develop such models using Multi-objective Optimization techniques based on Genetic Algorithms.
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Abstract: There is no generally accepted metric for measuring the performance of anaphora resolution systems, and the existing metrics—MUC, B3, CEAF, Blanc, among others—tend to reward significantly different behaviors. Systems optimized according to one metric tend to perform poorly with respect to other ones, making it very difficult to compare anaphora resolution systems, as clearly shown by the results of the SEMEVAL 2010 Multilingual Coreference task. One solution would be to find a single completely satisfactory metric, but it’s not clear whether this is possible and at any rate it is not going to happen any time soon. An alternative is to optimize models according to multiple metrics simultaneously. In this paper, we show, first of all, that this is possible to develop such models using Multi-objective Optimization (MOO) techniques based on Genetic Algorithms. Secondly, we show that optimizing according to multiple metrics simultaneously may result in better results with respect to each individual metric than optimizing according to that metric only.
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Citations
•Proceedings Article
Joint Conference on EMNLP and CoNLL - Shared Task
Sameer Pradhan,Alessandro Moschitti,Nianwen Xue +2 more
- 01 Jul 2012
41
Differential evolution-based feature selection technique for anaphora resolution
Utpal Kumar Sikdar,Asif Ekbal,Sriparna Saha,Olga Uryupina,Massimo Poesio +4 more
- 01 Aug 2015
TL;DR: A differential evolution (DE)-based feature selection technique is developed for anaphora resolution in a resource-poor language, namely Bengali and a number of models for mention detection based on machine learning and heuristics are developed.
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A comprehensive review on feature set used for anaphora resolution
TL;DR: This paper presents a review of AR approaches based on significant features utilized to perform this task and presents the evaluation metrics for this field and provides the state-of art for the better understanding of solving AR problem from the feature selection perspective.
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•Proceedings Article
BART goes multilingual: The UniTN / Essex submission to the CoNLL-2012 Shared Task
Olga Uryupina,Alessandro Moschitti,Massimo Poesio +2 more
- 13 Jul 2012
TL;DR: A novel entity-mention detection algorithm is proposed that might help identify nominal mentions in an unknown language and the impact of basic linguistic information on the overall performance level of the coreference resolution system is discussed.
•Proceedings Article
Multi-metric optimization for coreference: The UniTN / IITP / Essex submission to the 2011 CONLL Shared Task
Olga Uryupina,Sriparna Saha,Asif Ekbal,Massimo Poesio +3 more
- 23 Jun 2011
TL;DR: Multi-objective function Optimization techniques based on Genetic Algorithms to optimize models according to multiple metrics simultaneously simultaneously are investigated.
11
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