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Measuring Similarity of Academic Articles with Semantic Profile and Joint Word Embedding

查看全文 作　　者：Ming [1]Liu;Bo [1]Lang;Zepeng [1]Gu;Ahmed [1]Zeeshan 高影响力作者 机构地区：[1]State Key Laboratory of Soflware Development Environment, Beihang University, Beijing 100191, China高影响力机构 出　　处：《Tsinghua Science and Technology》索引2017年第22卷第6期,共14页高影响力期刊 基　　金：supported by the Foundation of the State Key Laboratory of Software Development Environment(No.SKLSDE-2015ZX-04) 摘　　要：Long-document semantic measurement has great significance in many applications such as semantic searchs, plagiarism detection, and automatic technical surveys. However, research efforts have mainly focused on the semantic similarity of short texts. Document-level semantic measurement remains an open issue due to problems such as the omission of background knowledge and topic transition. In this paper, we propose a novel semantic matching method for long documents in the academic domain. To accurately represent the general meaning of an academic article, we construct a semantic profile in which key semantic elements such as the research purpose, methodology, and domain are included and enriched. As such, we can obtain the overall semantic similarity of two papers by computing the distance between their profiles. The distances between the concepts of two different semantic profiles are measured by word vectors. To improve the semantic representation quality of word vectors, we propose a joint word-embedding model for incorporating a domain-specific semantic relation constraint into the traditional context constraint. Our experimental results demonstrate that, in the measurement of document semantic similarity, our approach achieves substantial improvement over state-of-the-art methods, and our joint word-embedding model produces significantly better word representations than traditional word-embedding models. 关 键 词：document semantic similarity text understanding semantic enrichment word embedding scientific literature analysis
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