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An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining
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DOI：10.1109/TCBB.2015.2478467.png)
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Abstract
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In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words using deeper information, we make explicit the semantic regularity to emerge in word relations, including dependency relations and context relations, and propose a novel architecture for computing continuous vector representation by leveraging those relations. The performance of our model is measured on word analogy task and Protein-Protein Interaction Extraction (PPIE) task. Experimental results show that our method performs overall better than other word representation models on word analogy task and have many advantages on biomedical text mining.
Keywords:
Natural language processing
machine learning
connectionism and neural nets
object representation
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