Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

Abstract

We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific features on a large unlabeled corpus. This allows us to explicitly incorporate relationspecific information into the word embeddings. The learned word embeddings are then used to construct feature vectors for a relation classification model. On a wellestablished semantic relation classification task, our method significantly outperforms a baseline based on a previously introduced word embedding method, and compares favorably to previous state-of-the-art models that use syntactic information or manually constructed external resources.

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@inproceedings{Hashimoto2015TaskOrientedLO, title={Task-Oriented Learning of Word Embeddings for Semantic Relation Classification}, author={Kazuma Hashimoto and Pontus Stenetorp and Makoto Miwa and Yoshimasa Tsuruoka}, booktitle={CoNLL}, year={2015} }