Abstract
In this paper we propose a named entity recognizer (NER) which we can train from partially annotated data. As the natural language processing is getting to be applied to diverse texts, there arise high demands for the NER for new named entity (NE) definition in different domains. For these special NE definitions, only a small annotated corpus is available in the beginning, and a rapid and low-cost development of an NER is needed in practice. To satisfy the needs, we propose the use of partially annotated data, which is a set of sentences in which only a limited number of words are annotated with NE tags. Our NER method uses two-pass search for sequential labeling of NE tags: (1) enumerate NE tags with confidences for each word independently from the tags for other words and (2) the best NE tag sequence search referring to the tag-confidence pairs by CRFs. For the first-pass module, our method uses partially annotated data to improve the accuracy in the target domain. By this two-pass search framework, our method is expected to incorporate tag sequence statistics and to outperform state-of-the-art NERs based on a sequence labeling while keeping the high domain adaptability. We conducted several experiments comparing state-of-the-art NERs in various scenarios. The results showed that our method is effective both in the normal case and in adaptation cases.
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Notes
- 1.
We can also use so-called leaving-one-out technique [9], but it is computationally too costly because we have to build as many models as the number of words in the training data.
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- 4.
This is a simulation and does not include real annotation work. An experiment with the real annotation time is a future work.
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Acknowledgments
This work was supported by JSPS Grants-in-Aid for Scientific Research Grant, and JSPS Grant-in-Aid for Young Scientists Grant. We are grateful to the annotators for their contribution to the design of the guidelines and the annotation effort.
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Sasada, T., Mori, S., Kawahara, T., Yamakata, Y. (2016). Named Entity Recognizer Trainable from Partially Annotated Data. In: Hasida, K., Purwarianti, A. (eds) Computational Linguistics. PACLING 2015. Communications in Computer and Information Science, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-10-0515-2_11
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