Active Learning for Extracting Technical Terms Covering Multiword Phrases
Fumimaro Odakura, Koga Kobayashi, Kei Wakabayashi · 2021
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.