Abstract
Traditional NLP model for readability assessment represents document as vector of words or vector of linguistic features that may be sparse, discrete, and ignoring the latent relations among features. We observe from data and linguistics theory that a document’s linguistic features are not necessarily conditionally independent. To capture the latent relations among linguistic features, we propose to build feature graphs and learn distributed representation with Statistical Relational Learning. We then project the document vectors onto the linguistic feature embedding space to produce linguistic feature knowledge-enriched document representation. We showcase this idea with Chinese L1 readability classification experiments and achieve positive results. Our proposed model performs better than traditional vector space models and other embedding based models for current data set and deserves further exploration.
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Acknowledgements
This work was supported by National Social Science Fund (Grant No. 17BGL068). We thank Prof. Jianyun Nie and anonymous reviewers for their valuable suggestions and thoughtful feedback. We thank undergraduate students Zhiwei Wu, Yuansheng Wang, Xu Zhang, Yuan Chen, Hanwu Chen, Licong Tan, and Hao Zhang for their helpful assistance and support.
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Qiu, X., Lu, D., Shen, Y., Cai, Y. (2019). Linguistic Feature Representation with Statistical Relational Learning for Readability Assessment. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_32
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DOI: https://doi.org/10.1007/978-3-030-32236-6_32
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