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- W2774160914 abstract "A common reason for errors in coreference resolution is the lack of semantic information to help determine the compatibility between mentions referring to the same entity. Distributed representations, which have been shown successful in encoding relatedness between words, could potentially be a good source of such knowledge. Moreover, being obtained in an unsupervised manner, they could help address data sparsity issues in labeled training data at a small cost. In this work we investigate whether and to what extend features derived from word embeddings can be successfully used for supervised coreference resolution. We experiment with several word embedding models, and several different types of embeddingbased features, including embedding cluster and cosine similarity-based features. Our evaluations show improvements in the performance of a supervised state-of-theart coreference system." @default.
- W2774160914 created "2017-12-22" @default.
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- W2774160914 date "2017-11-10" @default.
- W2774160914 modified "2023-09-24" @default.
- W2774160914 title "Word Embeddings as Features for Supervised Coreference Resolution" @default.
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- W2774160914 doi "https://doi.org/10.26615/978-954-452-049-6_088" @default.
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