Abstract
Information extraction from microblogs has recently attracted researchers in the fields of knowledge discovery and data mining owing to its short nature. Annotating data is one of the significant issues in applying machine learning approaches to these sources. Active learning (AL) and semi-supervised learning (SSL) are two distinct approaches to reduce annotation costs. The SSL approach exploits high-confidence samples and AL queries the most informative samples. Thus they can produce better results when jointly applied. This paper proposes a combination of AL and SSL to reduce the labeling effort for named entity recognition (NER) from tweet streams by using both machine-labeled and manually-labeled data. The AL query algorithms select the most informative samples to label those done by a human annotator. In addition, Conditional Random Field (CRF) is chosen as an underlying model to select high-confidence samples. The experiment results on a tweet dataset demonstrate that the proposed method achieves promising results in reducing the human labeling effort and that it can significantly improve the performance of NER systems.
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This work was supported by the BK21+ program of the National Research Foundation (NRF) of Korea.
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Tran, V.C., Hoang, D.T., Nguyen, N.T., Hwang, D. (2017). A Hybrid Method for Named Entity Recognition on Tweet Streams. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_25
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DOI: https://doi.org/10.1007/978-3-319-54472-4_25
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