逻辑学:开放域信息提取的统一端到端神经方法

Mingming Sun, Xu Li, Xin Wang, M. Fan, Yue Feng, Ping Li
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引用次数: 51

摘要

本文研究了在开放域中从句子中提取实体级和关系级中间结构的开放信息抽取问题。本文重点分析了四种有价值的中间结构(关系、属性、描述和概念),并提出了统一的知识表达形式SAOKE。我们公开发布了一个包含48248个句子和相应事实的数据集,数据集采用众包标注的SAOKE格式。据我们所知,这是用于开放信息提取任务的最大的公开可用的人类标记数据集。使用这个标记的SAOKE数据集,我们使用序列到序列范式(称为Logician)训练端到端神经模型,将句子转换为事实。对于每个句子,与现有算法一般只关注提取单个事实而不考虑其他可能的事实不同,Logician对所有可能涉及的事实进行全局优化,其中事实之间既相互竞争以吸引单词的注意,又相互合作以共享单词。通过对各种类型的开放域关系提取任务的实验研究,揭示了逻辑学算法相对于其他先进算法的一贯优势。实验验证了SAOKE格式的合理性、SAOKE数据集的价值、提出的Logician模型的有效性,以及将端到端学习范式应用于监督数据集的可行性,以解决开放信息提取的挑战性任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains 48,248 sentences and the corresponding facts in the SAOKE format labeled by crowdsourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequence-to-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.
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