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Translations Diversification for Expert Finding: A Novel Clustering-based Approach

机译：专家寻找的翻译多样化：一种基于聚类的新颖方法

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摘要

Expert finding is the task of retrieving and ranking knowledgeable people in the subject of user's query. It is a well-studied problem that has attracted the attention of many researchers. The most important challenge in expert finding is to determine the similarity between query words and documents authored by candidate experts. One of the most important challenges in Information Retrieval (IR) community is the issue of vocabulary gap between queries and documents. In this study, a translation model based on words clustering in two query and co-occurrence spaces is proposed to overcome this problem. First, the words that are semantically close, are clustered in a query space and then each cluster in this space are clustered again in a co-occurrence space. Representatives of each cluster in the co-occurrence space are considered as a diverse subset of the parent cluster. By this method, the query translations are expected to be diversified in the query space. Next, a probabilistic model, that is based on the belonging degree of word to cluster and similarity of cluster to query in the query space, is used to consider the problem of vocabulary gap. Finally, the corresponding translations to each query are used in conjunction with a combination model for expert finding. Experiments on Stack Overflow dataset show the effectiveness of the proposed method for expert finding.
机译：专家查找是在用户查询的主题中检索知识丰富的人并对其进行排名的任务。这是一个经过充分研究的问题，吸引了许多研究人员的注意力。专家发现中最重要的挑战是确定查询词与候选专家撰写的文档之间的相似性。信息检索（IR）社区中最重要的挑战之一是查询和文档之间的词汇间隔问题。在这项研究中，提出了一种基于单词聚类的两个查询和共现空间的翻译模型来解决这个问题。首先，将语义上接近的单词聚集在查询空间中，然后将该空间中的每个群集再次聚集在同现空间中。共现空间中每个群集的代表被视为父群集的不同子集。通过这种方法，查询翻译有望在查询空间中多样化。接下来，基于单词在聚类空间中的归属程度和聚类在查询空间中的相似度的概率模型，考虑了词汇间隙问题。最后，每个查询的相应翻译与组合模型一起用于专家查找。在Stack Overflow数据集上进行的实验证明了该方法对于专家发现的有效性。

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