Unlocking the Power of Word2Vec for Identifying Implicit Links

Gabriel Gutu, M. Dascalu, Stefan Ruseti, Traian Rebedea, Stefan Trausan-Matu
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引用次数: 4

Abstract

This paper presents a research on using Word2Vec for determining implicit links in multi-participant Computer-Supported Collaborative Learning chat conversations. Word2Vec is a powerful and one of the newest Natural Language Processing semantic models used for computing text cohesion and similarity between documents. This research considers cohesion scores in terms of the strength of the semantic relations established between two utterances, the higher the score, the stronger the similarity between two utterances. An implicit link is established based on cohesion to the most similar previous utterance, within an imposed window. Three similarity formulas were used to compute the cohesion score: an unnormalized score, a normalized score with distance and Mihalcea's formula. Our corpus of conversations incorporated explicit references provided by authors, which were used for validation. A window of 5 utterances and a 1-minute time frame provided the highest detection rate both for exact matching and matching of a block of continuous utterances belonging to the same speaker. Moreover, the unnormalized score correctly identified the largest number of implicit links.
解锁Word2Vec识别隐含链接的能力
本文介绍了使用Word2Vec来确定多参与者计算机支持的协作学习聊天会话中的隐式链接的研究。Word2Vec是一个功能强大的最新的自然语言处理语义模型,用于计算文档之间的文本内聚和相似度。本研究从两个话语之间建立的语义关系的强弱来考虑衔接分数,分数越高,两个话语之间的相似性越强。在一个强加的窗口内,根据与最相似的先前话语的衔接建立隐式链接。采用三种相似度公式计算衔接分数:非归一化分数、带距离归一化分数和Mihalcea公式。我们的对话语料库包含作者提供的明确参考,用于验证。5个话语的窗口和1分钟的时间段对于精确匹配和同一说话人连续话语块的匹配都提供了最高的检测率。此外,非标准化分数正确地识别了最大数量的隐式链接。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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