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首页> 外文会议>22nd International Conference on Computational Linguistics >CollabRank: Towards a Collaborative Approach to Single-Document Keyphrase Extraction
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CollabRank: Towards a Collaborative Approach to Single-Document Keyphrase Extraction

机译：CollabRank：迈向单文档关键字提取的协作方法

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摘要

Previous methods usually conduct the keyphrase extraction task for single documents separately without interactions for each document, under the assumption that the documents are considered independent of each other. This paper proposes a novel approach named Col-labRank to collaborative single-document keyphrase extraction by making use of mutual influences of multiple documents within a cluster context. CollabRank is implemented by first employing the clustering algorithm to obtain appropriate document clusters, and then using the graph-based ranking algorithm for collaborative single-document keyphrase extraction within each cluster. Experimental results demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the system performance relies positively on the quality of document clusters.
机译：在假定文档被认为彼此独立的前提下，以前的方法通常单独为单个文档执行关键字提取任务，而无需为每个文档进行交互。本文提出了一种名为Col-labRank的新颖方法，该方法可通过利用群集上下文中多个文档的相互影响来进行协作式单文档关键字短语提取。首先通过采用聚类算法来获取适当的文档聚类，然后使用基于图的排名算法来实现每个聚类中的协作单文档关键词提取来实现CollabRank。实验结果证明了所提出方法的令人鼓舞的性能。已经研究了不同的聚类算法，我们发现系统性能与文档聚类的质量成正比。

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