Relation classification is the task to predict semantic relations between pairs of entities in a given text. In this paper, a novel Long Short Term Memory Network (LSTM)-based approach is proposed to extract relations between entities in Chinese text. The shortest dependency path (SDP) between two entities, together with the various selected features in the path, are first extracted, and then used as input of an LSTM model to predict the relation between them. The performance of the system was evaluated on the ACE 2005 Multilingual Training Corpus, and achieved a state-of-the-art F-measure of 87.87% on six general type relations and 83.40% on eighteen subtype relations in this corpus.
@InProceedings{ZHANG18.153, author = {Linrui Zhang and Dan Moldovan}, title = "{Chinese Relation Classification using Long Short Term Memory Networks}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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