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Semantic Dependency Labeling of Chinese Noun Phrases Based on Semantic Lexicon

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (NLP-NABD 2017, CCL 2017)

Abstract

We have presented a simple algorithm to noun phrases interpretation based on hand-crafted knowledge-base containing detailed semantic information. The main idea is to define a set of relations that can hold between the words and use a semantic lexicon including semantic classifications and collocation features to automatically assign relations to noun phrases. We divide the NPs into two kinds of types: NPs with one verb or non-consecutive verbs and NPs with consecutive verbs, and design two different labeling methods according to their syntactic and semantic features. For the first kind of NPs we report high precision, recall and F-score on a dataset with nine semantic relations, and for the second type the results are also promising on a dataset with four relations. We create a valuable manually-annotated resource for noun phrases interpretation, which we make publicly available with the hope to inspire further research in noun phrases interpretation.

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Acknowledgments

Thanks to National Natural Science Foundation of China (NSFC) via Grant 61170144, Major Program of China’s National Linguistics Work Committee during the twelfth five-year plan (ZDI125-41), Young and Middle Aged Academic Cadre Support Plan of Beijing Language and Culture University (501321303), Graduate Innovation Foundation in 2017 (17YCX137).

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Correspondence to Yanqiu Shao .

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Li, Y., Shao, Y., Yang, H. (2017). Semantic Dependency Labeling of Chinese Noun Phrases Based on Semantic Lexicon. 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_20

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  • DOI: https://doi.org/10.1007/978-3-319-69005-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69004-9

  • Online ISBN: 978-3-319-69005-6

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