Gongye Jin


2017

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Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames
Gongye Jin | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

This paper presents a method for applying automatically acquired knowledge to semantic role labeling (SRL). We use a large amount of automatically extracted knowledge to improve the performance of SRL. We present two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.

2015

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Chinese Semantic Role Labeling using High-quality Syntactic Knowledge
Gongye Jin | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

2014

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A Framework for Compiling High Quality Knowledge Resources From Raw Corpora
Gongye Jin | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The identification of various types of relations is a necessary step to allow computers to understand natural language text. In particular, the clarification of relations between predicates and their arguments is essential because predicate-argument structures convey most of the information in natural languages. To precisely capture these relations, wide-coverage knowledge resources are indispensable. Such knowledge resources can be derived from automatic parses of raw corpora, but unfortunately parsing still has not achieved a high enough performance for precise knowledge acquisition. We present a framework for compiling high quality knowledge resources from raw corpora. Our proposed framework selects high quality dependency relations from automatic parses and makes use of them for not only the calculation of fundamental distributional similarity but also the acquisition of knowledge such as case frames.

2013

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High Quality Dependency Selection from Automatic Parses
Gongye Jin | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the Sixth International Joint Conference on Natural Language Processing