基于共指消解的实体搜索模型研究

A Coreference Resolution Based Entity Search Model

实体属性挖掘(slot filling,SF)旨在从大规模文档集中挖掘给定实体(称作查询)的特定属性信息。实体搜索是SF的重要组成部分,负责检索包含给定查询的文档(称为相关文档),供后续模块从中抽取属性信息。目前,SF领域关于实体搜索的研究较少,使用的基于布尔逻辑的检索模型忽略了实体查询的特点,仅使用查询的词形信息,受限于查询歧义性,检索结果准确率较低。针对这一问题,该文提出一种基于跨文档实体共指消解(cross document coreference resolution,CDCR)的实体搜索模型。该方法通过对召回率较高但准确率较低的候选结果进行CDCR,过滤不包含与给定实体共指实体的文档,提高检索结果的准确率。为了降低过滤造成的召回率损失,该文使用伪相关反馈方法扩充查询实体的描述信息。实验结果显示,相比于基准系统,该方法能有效提升检索结果,准确率和F1分别提升5.63%、2.56%。

The goal of Slot Filling（SF）is extracting certain attribute value of given entity（query）from large scale corpus.Entity search,as an important component of SF,retrieves documents referring to the given entity for other components to extracting attribute values from them.In contrast to the existing entity search based on boolean logic,we propose a cross document coreference resolution（CDCR）based entity search model.This CDCR improves the precision of IR results by filtering documents which do not contain mentions referring to the given entity.To minimize the loss of recall in filtering process,we introduce the pseudo relevant feedback method to augment the information of given entity.Experimental results show that our model outperforms the baseline by increasing the precision and F1 score by 5.63% and 2.56%,respectively.