Research Area:  Machine Learning

Abstract:

Determining semantic similarity between academic documents is crucial to many tasks such as plagiarism detection, automatic technical survey and semantic search. Current studies mostly focus on semantic similarity between concepts, sentences and short text fragments. However, document-level semantic matching is still based on statistical information in surface level, neglecting article structures and global semantic meanings, which may cause the deviation in document understanding. In this paper, we focus on the document-level semantic similarity issue for academic literatures with a novel method. We represent academic articles with topic events that utilize multiple information profiles, such as research purposes, methodologies and domains to integrally describe the research work, and calculate the similarity between topic events based on the domain ontology to acquire the semantic similarity between articles. Experiments show that our approach achieves significant performance compared to state-of-the-art methods.

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Author(s) Name:  Ming Liu, Bo Lang, Zepeng Gu

Publisher name:  arXiv:1711.11508

DOI:  10.48550/arXiv.1711.11508

Volume Information:  

Paper Link:   https://arxiv.org/abs/1711.11508