基于篇章级事件表示的文本相关度计算方法

Along with the explosion of web information, information flow service has attracted the attention of users. In this kind of service, how to measure the correlation between texts and further filter the redundant information collected from multiple sources becomes the key solution to meet the user's desire. Recently, the popular text correlation calculation methods mostly represent text as vector and then measure text similarity as text correlation. However, in information flow service, most of the texts are news, and the core element in a news is the event it stated. Therefore, we need a way to extract the core features that are related to the event stated by text, so we can accurately calculate text correlation via these extracted features. Unfortunately, recent event-related researches focus on the sentence-level. To calculate text correlation, we need to grasp the content of the text from the passage-level, which indicates that passage-level event analysis has more impact. To this end, we propose a passage-level event representation method based on sentence-level event extraction. It constructs a passage-level event connection graph based on the extracting results obtained from sentences. After that, it selects the important nodes in the graph as the representations of the passage-level events. Based on the passage-level representations, we can acquire text correlation. Experimental results indicate that our method outperforms conventional text correlation calculation methods.