Zhao, Y.; Liu, Q.-W.; Hu F.-H.; Lu, G., and Zhang, T., 2019. Synonym extraction in shipping industry with distant supervision and deep neural network. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 455–459. Coconut Creek (Florida), ISSN 0749-0208.

Current synonym extraction methods, including lexical pattern based methods, distributional similarity-based methods, and search engine based methods, usually need a lot of manual work, either in rule editing or in training corpus labeling. This paper tries to find synonyms in a “self-supervised” way and presents a synonym extraction framework based on distant supervision. The model used here is LSTM and CRF. We first analyze the nature of the synonym representations of the Nautical Navigation in text; then automatically generate labeled training samples by using structured knowledge (a domain dictionary) and some generic heuristic rules. Finally, we train some “LSTM + CRF” models and use them to extract synonyms in the shipping industry corpus. More than 100 thousand facts are successfully extracted, which contain 3,629 distinct pairs of synonyms; and the precision is 0.967.