1998 AStatisticalApproachToAnaphoraResolution
- (Ge et al., 1998) ⇒ Niyu Ge, John Hale, Eugene Charniak. (1998). “A Statistical Approach to Anaphora Resolution.” In: Proceedings of the Sixth Workshop on Very Large Corpora.
Subject Headings: Supervised Anaphora Resolution Algorithm.
Notes
- It is an early application of Supervised Learning to Anaphora Resolution.
- It proposes a small Feature Set and simple probabilities.
- It achieve acceptable performance.
- It trains on a customer version of the Penn Tree Bank.
- It tries to Impute and use Person Sex information.
- A C implementation can be found at http://www.cl.cam.ac.uk/~jp233/software/
Cited By
2001
- (Soon et al., 2001) ⇒ Wee Meng Soon, Hwee Tou Ng, and Daniel Chung Yong Lim. (2001). “A Machine Learning Approach to Coreference Resolution of Noun Phrases.” In: Computational Linguistics, Vol. 27, No. 4.
Quotes
Abstract
This paper presents an algorithm for identifying pronominal anaphora and two experiments based upon this algorithm. We incorporate multiple anaphora resolution factors into a statistical framework -- specifically the distance between the pronoun and the proposed antecedent, gender/number/animaticity of the proposed antecedent, governing head information and noun phrase repetition. We combine them into a single probability that enables hs to identify the referent. Our first experiment shows the relative contribution of each source Of information and demonstrates a success rate of 82.9% for all sources combined. The second experiment investigates a method for unsuper- vised learning of gender/number/animaticity information. We present some experiments illustrating the accuracy of the method and note that with this information added, our pronoun resolution method achieves 84.2% accuracy.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1998 AStatisticalApproachToAnaphoraResolution | Eugene Charniak Niyu Ge John Hale | A Statistical Approach to Anaphora Resolution | http://acl.ldc.upenn.edu/W/W98/W98-1119.pdf |