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Web Reviews and Events Matching Based on Event Feature Segments and Semi-Markov Conditional Random Fields

机译：基于事件特征段和半马尔可夫条件随机字段的Web评论和事件匹配

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摘要

To establish links between a large number of reviews and events, we propose a web reviews and events matching approach by event feature segments and semi-Markov conditional random fields (CRFs). We extract named entities and verb phrases from reviews as event feature segments. We use semi-Markov CRFs to label the reviews and to recognize event feature segments at the segment level. This approach uses event feature segments to match reviews and events. Therefore, it is more accurate than other approaches which use only named entities to match. We use several feature rules to recognize the variants of named entities, such as abbreviation and acronym. In addition, we use phrase dependency parsing tree to recognize verb phrases. A compositive similarity measurement function is presented to combine similarity results of event feature segments. Experimental results demonstrate that this method can accurately match reviews and events.
机译：为了在大量评论和事件之间建立链接，我们提出了一种通过事件特征段和半马尔可夫条件随机字段（CRF）进行网络评论和事件匹配的方法。我们从评论中提取命名实体和动词短语作为事件特征片段。我们使用半马尔可夫CRF标记评论并在细分级别识别事件特征细分。这种方法使用事件特征段来匹配评论和事件。因此，它比仅使用命名实体进行匹配的其他方法更为准确。我们使用一些特征规则来识别命名实体的变体，例如缩写和首字母缩写。另外，我们使用短语依赖分析树来识别动词短语。提出了一种综合相似度度量函数来组合事件特征片段的相似度结果。实验结果表明，该方法可以准确匹配评论和事件。

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