Abstract: Event detection mainly focuses on event trigger recognition from unstructured text to achieve correct classification of event types. Compared with English, there is no natural separation in Chinese, and word segmentation boundaries need to be determined before lexicon information can be used. In addition, there is a word-trigger mismatch problem in Chinese event detection. According to the characteristics of Chinese and event detection, a Chinese event detection based on multi-lexicon feature augmentation is proposed, which introduces a word collection containing multi-word information into the character-based model through an external dictionary to utilize the semantic information of multiple segmentation results. At the same time, static text word frequency statistics and Chinese word segmentation systems are used to make collaborative decision on the weight of words in the lexical set to obtain more accurate lexical semantics. The experimental results on ACE2005 Chinese dataset show that the proposed method achieves the best performance, which verifies its effectiveness in Chinese event detection.

Key words:  Chinese event detection, feature augmentation, multiple lexicon feature, lexicon weight determination