论文标题
探索语义空间作为单词关联模型的适用性，以提取语义关系
Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships
论文作者
论文摘要
鉴于自然语言处理（NLP）的最新进展和进展，在过去几年中，语义关系的提取一直是研究议程的首要任务。这项工作主要是由于构建知识图（KG）和基地（KB）作为智能应用程序的关键要素是一个永无止境的挑战，因为在需要修改旧知识的同时需要收获新知识。当前，从文本中提取的关系的方法是由神经模型主导的，从大型语料库进行机器学习中的某种遥远（弱）监督，或者没有咨询外部知识来源。在本文中，我们经验研究和探讨了使用经典语义空间和模型的新思想的潜力，例如单词嵌入，生成用于提取单词关联的单词嵌入，以及关系提取方法。目的是使用这些单词关联模型来加强当前的关系提取方法。我们认为，这是这种第一次尝试，研究的结果应该阐明可以使用这些单词关联模型以及最有前途的类型的关系的程度。
Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and bases (KB), as a key ingredient of intelligent applications, is a never-ending challenge, since new knowledge needs to be harvested while old knowledge needs to be revised. Currently, approaches towards relation extraction from text are dominated by neural models practicing some sort of distant (weak) supervision in machine learning from large corpora, with or without consulting external knowledge sources. In this paper, we empirically study and explore the potential of a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association, in conjunction with relation extraction approaches. The goal is to use these word association models to reinforce current relation extraction approaches. We believe that this is a first attempt of this kind and the results of the study should shed some light on the extent to which these word association models can be used as well as the most promising types of relationships to be considered for extraction.
