2020/05/13 by Artuur Leeuwenberg, Marie-Francine Moens, Leeuwenberg, Artuur +2 · 3 citations
Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling #cs.AI #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2005.06527
Extended abstract of a JAIR article, which is to appear in the proceedings of IJCAI 2020 (the copyright of this abstract is held by IJCAI 2020)
openalex publication_date 2020/05/13 · arxiv created 2020/05/15 · arxiv updated 2020/05/18 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.