计算机科学
阈值
事件(粒子物理)
人工智能
班级(哲学)
背景(考古学)
集合(抽象数据类型)
相似性(几何)
词(群论)
任务(项目管理)
代表(政治)
注释
自然语言处理
模式识别(心理学)
图像(数学)
古生物学
语言学
哲学
物理
管理
量子力学
政治
政治学
法学
经济
生物
程序设计语言
作者
Aboubacar Tuo,Romaric Besançon,Olivier Ferret,Julien Tourille
标识
DOI:10.1007/978-3-031-28238-6_55
摘要
Recent studies in few-shot event trigger detection from text address the task as a word sequence annotation task using prototypical networks. In this context, the classification of a word is based on the similarity of its representation to the prototypes built for each event type and for the “non-event” class (also named null class). However, the “non-event” prototype aggregates by definition a set of semantically heterogeneous words, which hurts the discrimination between trigger and non-trigger words. We address this issue by handling the detection of non-trigger words as an out-of-domain (OOD) detection problem and propose a method for dynamically setting a similarity threshold to perform this detection. Our approach increases f-score by about 10 points on average compared to the state-of-the-art methods on three datasets.
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