LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

We explore the capabilities of a bidirectional LSTM model to learn representations of word senses from semantically annotated corpora. We show that the utilization of an architecture that is aware of word order, like an LSTM, enables us to create better representations. We assess our proposed model on various standard benchmarks for evaluating semantic representations, reaching state-of-the-art performance on the SemEval-2014 word-to-sense similarity task.

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