Earticle

초록

영어

Semantic similarity is a core technique of many NLP research fields. However, state-of-the-art metrics for semantic similarity computation often operate at different levels, e.g., words or sentences. In this paper, semantic similarity computation metrics are firstly introduced and the quality is measured in order to determine their advantages and limitations; then a new semantic similarity metric based on multi-features fusion is proposed. Distributed representations of words are used for alignment-based disambiguation operation, and Wikipedia tags are used to enhance the performance of our approach. The proposed metric is unsupervised, and can be applied at different levels e.g., single words or entire documents. The metric is evaluated on both English and Chinese datasets, it is shown that the precision and recall scores are higher than metrics which simply using knowledge base or distributed representation of words.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Supervised Metrics
  2.2. Unsupervised Metrics
 3. Multi-Features Based Similarity Metrics
  3.1. Word Representations in Vector Space
  3.2. Alignment-Based Disambiguation
  3.3. Unsupervised Wikipedia Tags Learning Algorithm
 4. Experiments
  4.1. Experiment Preparation
  4.2. Evaluation Metric
  4.3. Experimental Results
 Acknowledgements
 References

키워드

저자

• Bo Ma [ Research Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences ]
• Yating Yang [ Research Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences ]
• Fan Zhao [ Research Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences ]
• Rui Dong [ Research Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences ]
• Xi Zhou [ Research Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605