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Semantic Similarity Measurement Using Knowledge-Augmented Multiple-prototype Distributed Word Vector

Semantic Similarity Measurement Using Knowledge-Augmented Multiple-prototype Distributed Word Vector

Wei Lu, Kailun Shi, Yuanyuan Cai, Xiaoping Che
Copyright: © 2016 |Volume: 8 |Issue: 2 |Pages: 13
ISSN: 1941-8663|EISSN: 1941-8671|EISBN13: 9781466690615|DOI: 10.4018/IJITN.2016040105
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MLA

Lu, Wei, et al. "Semantic Similarity Measurement Using Knowledge-Augmented Multiple-prototype Distributed Word Vector." IJITN vol.8, no.2 2016: pp.45-57. http://doi.org/10.4018/IJITN.2016040105

APA

Lu, W., Shi, K., Cai, Y., & Che, X. (2016). Semantic Similarity Measurement Using Knowledge-Augmented Multiple-prototype Distributed Word Vector. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 8(2), 45-57. http://doi.org/10.4018/IJITN.2016040105

Chicago

Lu, Wei, et al. "Semantic Similarity Measurement Using Knowledge-Augmented Multiple-prototype Distributed Word Vector," International Journal of Interdisciplinary Telecommunications and Networking (IJITN) 8, no.2: 45-57. http://doi.org/10.4018/IJITN.2016040105

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Abstract

Recent years, textual semantic similarity measurements play an important role in Natural Language Processing. The semantic similarity between concepts or terms can be measured by various resources like corpora, ontologies, taxonomies, etc. With the development of deep learning, distributed vector models are constructed for extracting the latent semantic information from corpora. Most of existing models create a single prototype vector to represent the meaning of a word such as CBOW. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. In this work, the authors propose a knowledge-augmented multiple-prototype model by using corpora and ontologies. Based on the distributed word vector learned by the CBOW model, the authors append the concept definition and the relational knowledge vector into the target word vector to enrich the semantic information of the word. Finally, the authors perform the experiments on well-known datasets to verify the efficiency of the authors' approach.

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