融合篇章表征的事件指代消解研究

事件指代消解任务比实体指代消解难度大, 主要原因为事件描述在非结构化文本中分布稀疏, 且不具备同指关系的单链占很大比例, 同时事件自身承载的语义信息比实体更加丰富。为了准确地抽取文本中的同指事件, 针对以上特点, 提出一种融合篇章表征的事件指代消解模型。该模型通过CRF有效地区分非事件句、单链以及同指链, 同时利用分层注意力机制捕捉句子级别和篇章级别的重要信息。在KBP2015和2016数据集上进行的事件指代消解实验验证了该模型的有效性, 在CoNLL评测标准下F1值达到43.07%。

Event Coreference Resolution with Document Representation

Event coreference resolution is more difficult than entity coreference resolution. The main reason is that the event mentions in the unstructured texts are sparse, and most of them do not have the coreference relationship, at the same time, the semantic information carried by the event itself is richer than entity. In order to accurately extract the coreferential events in the text, for the above characteristics of event coreference resolution, an event coreference resolution platform with text representation is proposed. This platform effectively distinguishes non-event mention, single-chain and coreference event mention through CRF, and uses hierarchical attention mechanism to capture important information at sentence level and text level. Experiments on KBP2015 and 2016 datasets verify the validity of the model, and the CoNLL evaluation standard reaches 43.07% of the F1 value.