Han Ren


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Finding Answers to Definition Questions Using Web Knowledge Bases
Han Ren | Donghong Ji | Jing Wan | Chong Teng
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Parsing Syntactic and Semantic Dependencies for Multiple Languages with A Pipeline Approach
Han Ren | Donghong Ji | Jing Wan | Mingyao Zhang
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task


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Automatic Chinese Catchword Extraction Based on Time Series Analysis
Han Ren | Donghong Ji | Jing Wan | Lei Han
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

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A Research on Automatic Chinese Catchword Extraction
Han Ren | Donghong Ji | Lei Han
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Catchwords refer to popular words or phrases within certain area in certain period of time. In this paper, we propose a novel approach for automatic Chinese catchwords extraction. At the beginning, we discuss the linguistic definition of catchwords and analyze the features of catchwords by manual evaluation. According to those features of catchwords, we define three aspects to describe Popular Degree of catchwords. To extract terms with maximum meaning, we adopt an effective ATE algorithm for multi-character words and long phrases. Then we use conic fitting in Time Series Analysis to build Popular Degree Curves of extracted terms. To calculate Popular Degree Values of catchwords, a formula is proposed which includes values of Popular Trend, Peak Value and Popular Keeping. Finally, a ranking list of catchword candidates is built according to Popular Degree Values. Experiments show that automatic Chinese catchword extraction is effective and objective in comparison with manual evaluation.