Abstract
Named entity recognition (NER) system is often realized using supervised methods such as CRF and LSTM-CRF. However, supervised methods often require large training data. In some low-resource languages, annotated data is often hard to obtain. Annotation projection method obtains annotated data from high-resource languages automatically. However, the data obtained automatically contains a lot of noise. In this paper, we propose a new data selection schema to select the high-quality sentences in annotated data. The data selection schema computes the sentence score considering the occurrence number of entity-tags and the minimum scores of entity-tags in sentences. The selected sentences can be used as an auxiliary annotated data in low resource languages. Experiments show that our data selection schema outperforms previous methods.
The work is supported by both National scientific and Technological Innovation Zero (No. 17-H863-01-ZT-005-005-01) and State’s Key Project of Research and Development Plan (No. 2016QY03D0505).
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Notes
- 1.
We consider English as high-resource language. Although Chinese is not truly low-resource language, we simulate the low-resource environment by limiting the size of training data which is similar to [14].
- 2.
- 3.
Before using the data from annotation projection in Chinese, we change the word tag schema to character tag schema. For example, the word ‘
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Hu, Y., Liao, M., Lv, P., Zheng, C. (2023). An Empirical Data Selection Schema in Annotation Projection Approach. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_2
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