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A Deeper Look into Dependency-Based Word Embeddings

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Authors: Sean MacAvaney, Amir Zeldes

Appeared in: Proceedings of the NAACL-HLT 2018 Student Research Workshop (NAACL Student Workshop 2018)

Links/IDs:
DOI 10.18653/v1/N18-4006 DBLP conf/naacl/MacAvaneyZ18 ACL N18-4006 arXiv 1804.05972 Google Scholar 7wWfoDgAAAAJ:UeHWp8X0CEIC Semantic Scholar f550b7bcd3731a13eaaa5033cb86f6f162c61d2f smac.pub naaclsrw2018-depwembed

Abstract:

We investigate the effect of various dependency-based word embeddings on distinguishing between functional and domain similarity, word similarity rankings, and two downstream tasks in English. Variations include word embeddings trained using context windows from Stanford and Universal dependencies at several levels of enhancement (ranging from unlabeled, to Enhanced++ dependencies). Results are compared to basic linear contexts and evaluated on several datasets. We found that embeddings trained with Universal and Stanford dependency contexts excel at different tasks, and that enhanced dependencies often improve performance.

BibTeX @inproceedings{macavaney:naaclsrw2018-depwembed, author = {MacAvaney, Sean and Zeldes, Amir}, title = {A Deeper Look into Dependency-Based Word Embeddings}, booktitle = {Proceedings of the NAACL-HLT 2018 Student Research Workshop}, year = {2018}, url = {https://arxiv.org/abs/1804.05972}, doi = {10.18653/v1/N18-4006}, pages = {40--45} }