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首页> 外文期刊>International journal of knowledge engineering and soft data paradigms >Event coreference resolution using particle swarm optimisation
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Event coreference resolution using particle swarm optimisation

机译:使用粒子群优化的事件共指解析

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摘要

Due to the tremendous increase of documents in the web, people who need information are not ready to spend much time in reading the entire content of documents retrieved. Instead they need precise information. A kind of precise information obtained in our work is event corefering sentences. All sentences referring to the same event instance are called event corefering sentences. Our proposed approach formulates this event coreference resolution as a graph-based clustering model. It constructs the graph based on the sentences in the document with edge weights representing similarity score between each pair of sentences. To reduce the number of singleton clusters and to have a balanced cut, our approach combines minimum conductance with cut clustering to form clusters of corefering sentences. As finding minimum conductance is NP-hard, it uses particle swarm optimisation technique to obtain minimum conductance.
机译:由于网络中文档的大量增加,需要信息的人还没有准备好花很多时间阅读检索到的文档的全部内容。相反,他们需要精确的信息。在我们的工作中获得的一种精确信息是事件相关语句。引用同一事件实例的所有句子都称为事件核心句。我们提出的方法将这种事件共参考分辨率公式化为基于图的聚类模型。它基于文档中的句子构造图,边缘权重表示每对句子之间的相似性得分。为了减少单例聚类的数量并实现均衡的裁切,我们的方法将最小电导与裁切聚类相结合以形成核心句的聚类。由于发现最小电导是NP困难的,因此它使用粒子群优化技术来获得最小电导。

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