Active Learning for Extracting Technical Terms Covering Multiword Phrases
Active Learning for Extracting Technical Terms Covering Multiword Phrases
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DOI：
10.1145/3487664.3487706
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Fumimaro Odakura;Koga Kobayashi;Kei Wakabayashi
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文献类型：
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作者：
Fumimaro Odakura;Koga Kobayashi;Kei Wakabayashi
术语自动抽取是组织文档的一项重要任务。虽然序列标记公式是主要的方法，但我们探索了一种方法，该方法将术语的示例作为输入并输出与给定术语相同类别的短语，以避免构建训练数据集的沉重成本。在这个方向上的现有方法是基于模板的，用户不能给系统任何反馈，即使一些提取的术语不是有意的。本文提出了一种采用主动学习方法，考虑用户反馈的技术术语抽取框架。该方法通过动态访问预先创建的倒排索引来提取由多个单词组成的术语。我们经验表明，所提出的方法相比，直接应用主动学习现有的方法的有效性。
Automatic extraction of technical terms is an important task for organizing a set of documents. While the sequence labeling formulation is the major approach, we explore a method that takes examples of terms as input and outputs phrases in the same category as the given terms to avoid the heavy cost for building a training dataset. The existing methods in this direction are template-based, which the user cannot give any feedback to the system even if some of the extracted terms are not intended. This paper proposes a framework for extracting technical terms that considers the user’s feedback by adopting active learning approach. The proposed method can extract terms consisting of multiple words by dynamically accessing an inverted index created in advance. We empirically show the effectiveness of the proposed method in comparison to the straightforward application of active learning to an existing method.