As a subtask of information extraction (IE), which aims to extract structured information from texts, event extraction is to recognize event trigger mentions of a predefined event type and their arguments. In general,...As a subtask of information extraction (IE), which aims to extract structured information from texts, event extraction is to recognize event trigger mentions of a predefined event type and their arguments. In general, event extraction can be divided into two subtasks: trigger extraction and argument extraction. Currently, the frequent existences of unannotated trigger mentions and poor-context trigger mentions impose critical challenges in Chinese trigger extraction. This paper proposes a novel three-layer joint model to integrate three components in trigger extraction, i.e., trigger identification, event type determination, and event subtype determination. In this way, different kinds of evidence on distinct pseudo samples can be well captured to eliminate the harmful effects of those un-annotated trigger mentions. In addition, this paper introduces various types of linguistically driven constraints on the trigger and argument semantics into the joint model to recover those poor-context trigger mentions. The experimental results show that our joint model significantly outperforms the state-of-the-art Chinese trigger extraction and Chinese event extraction as a whole.展开更多
文摘As a subtask of information extraction (IE), which aims to extract structured information from texts, event extraction is to recognize event trigger mentions of a predefined event type and their arguments. In general, event extraction can be divided into two subtasks: trigger extraction and argument extraction. Currently, the frequent existences of unannotated trigger mentions and poor-context trigger mentions impose critical challenges in Chinese trigger extraction. This paper proposes a novel three-layer joint model to integrate three components in trigger extraction, i.e., trigger identification, event type determination, and event subtype determination. In this way, different kinds of evidence on distinct pseudo samples can be well captured to eliminate the harmful effects of those un-annotated trigger mentions. In addition, this paper introduces various types of linguistically driven constraints on the trigger and argument semantics into the joint model to recover those poor-context trigger mentions. The experimental results show that our joint model significantly outperforms the state-of-the-art Chinese trigger extraction and Chinese event extraction as a whole.
