Making Sense of Word Embeddings
This work addresses the challenge of word sense disambiguation for natural language processing applications, but it is incremental as it builds on existing embedding methods.
The paper tackles the problem of learning word sense embeddings by inducing a sense inventory from existing word embeddings through clustering of ego-networks, and it achieves performance comparable to state-of-the-art unsupervised word sense disambiguation systems.
We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.