Abstract
Since the professional technical literature include amounts of complex noun phrases, identifying those phrases has an important practical value for such tasks as machine translation. Through analysis of those phrases in Chinese-English bilingual sentence pairs from the aircraft technical publications, we present an annotation specification based on the existing specification to label those phrases and a method for the complex noun phrase identification. In addition to the basic features including the word and the part-of-speech, we incorporate the word clustering features trained by Brown clustering model and Word Vector Class (WVC) model on a large unlabeled data into the machine learning model. Experimental results indicate that the combination of different word clustering features and basic features can leverage system performance, and improve the F-score by 1.83 % in contrast with the method only adding the basic features.
This work is supported by Humanities and Social Sciences Foundation for the Youth Scholars of Ministry of Education of China (№-14YJC740126) and National Natural Science Foundation of China (№-61402299).
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Xue, L., Zhang, G., Zhou, Q., Ye, N. (2015). Incorporating Word Clustering into Complex Noun Phrase Identification. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_3
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