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- W2964160783 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." @default.
- W2964160783 created "2019-07-30" @default.
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- W2964160783 date "2018-01-01" @default.
- W2964160783 modified "2023-09-23" @default.
- W2964160783 title "A Deeper Look into Dependency-Based Word Embeddings" @default.
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- W2964160783 doi "https://doi.org/10.18653/v1/n18-4006" @default.
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