Guiding Approximate Text Classification Rules via Context Information
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要Human experts can often easily write a set of approximate rules based on their domain knowledge for supporting automatic text classification. While such approximate rules are able to conduct classification at a general level, they are not effective for handling diverse and specific situations for a particular category. Given a set of approximate rules and a moderate amount of labeled data, existing incremental text classification learning models can be employed for tackling this problem by continuous rule refinement. However, these models lack the consideration of context information, which inherently exists in data. We propose a framework comprising rule embeddings and context embeddings derived from data to enhance the adaptability of approximate rules via considering the context information. We conduct extensive experiments and the results demonstrate that our proposed framework performs better than existing models in some benchmarking datasets, indicating that learning the context of rules is constructive for improving text classification performance.
出版社接受日期17.11.2018
著者Wai Chung Wong, Sunny Lai, Wai Lam, Kwong Sak Leung
會議名稱14th Asia Information Retrieval Societies Conference (AIRS)
會議開始日28.11.2018
會議完結日30.11.2018
會議地點Taipei
會議國家/地區台灣
會議論文集題名Proceedings of the 14th Asia Information Retrieval Societies Conference (AIRS)
出版年份2018
月份11
出版社Springer
頁次133 - 139
國際標準書號978-303003519-8
國際標準期刊號03029743
語言英式英語

上次更新時間 2020-23-10 於 01:09