In this work we evaluate domain-specific embedding models induced from textual resources in the Oil and Gas domain. We conduct intrinsic and extrinsic evaluations of both general and domain-specific embeddings and we observe that constructing domain-specific word embeddings is worthwhile even with a considerably smaller corpus size. Although the intrinsic evaluation shows low performance in synonymy detection, an in-depth error analysis reveals the ability of these models to discover additional semantic relations such as hyponymy, co-hyponymy and relatedness in the target domain. Extrinsic evaluation of the embedding models is provided by a domain-specific sentence classification task, which we solve using a convolutional neural network. We further adapt embedding enhancement methods to provide vector representations for infrequent and unseen terms. Experiments show that the adapted technique can provide improvements both in intrinsic and extrinsic evaluation.
@InProceedings{NOORALAHZADEH18.268, author = {Farhad Nooralahzadeh and Lilja Øvrelid and Jan Tore Lønning}, title = "{Evaluation of Domain-specific Word Embeddings using Knowledge Resources}", 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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