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Improving semantic similarity retrieval with word embeddings

机译:通过词嵌入改善语义相似度检索

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Word similarity matchmaking is one of the core research areas of information retrieval. The existingmethods based on a synonym dictionary would lead to the problem of semantic gap, whichcould be caused by the absence of synonyms. To address this problem, we improve semantic similarityretrieval by incorporating word embeddings. Especially, word embeddings are trained byWord2Vec and then use them to depict the semantic similarity between words. Experiments areconducted on two different datasets, ie, one is a public long text dataset (ie, Reuters-21578), andthe other is a short text dataset (ie, 120ask) collected from a healthcare community. The experimentalresults on the twodatasets showthat the proposed method further improves the accuracyof the similarity retrieval.
机译:词语相似度匹配是信息检索的核心研究领域之一。现有基于同义词词典的方法会导致语义鸿沟的问题,这可能是由于缺少同义词所致。为了解决这个问题,我们通过合并单词嵌入来提高语义相似度。特别地,单词嵌入由 r nWord2Vec训练,然后使用它们来描述单词之间的语义相似性。实验是在两个不同的数据集上进行的,即一个是公共长文本数据集(即Reuters-21578),另一个是从医疗保健社区收集的短文本数据集(即120ask)。对两个数据集的实验结果表明,该方法进一步提高了相似度检索的准确性。

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