Abstract
Tibetan syntactic functional chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic functional chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the functional chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic functional chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic functional chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (61671064, 61201352, and 61132009), the National Key Basic Research Program of China (2013CB329303) and the Fundamental Research Fund of Beijing Institute of Technology (20130742010).
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Shi, S., Liu, Y., Wang, T., Long, C., Huang, H. (2017). Tibetan Syllable-Based Functional Chunk Boundary Identification. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_36
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DOI: https://doi.org/10.1007/978-3-319-69005-6_36
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