文章基本信息

标题：Information Extraction Tool Text2ALM: From Narratives to Action Language System Descriptions
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作者：Craig Olson ; Yuliya Lierler
期刊名称：Electronic Proceedings in Theoretical Computer Science
电子版ISSN：2075-2180
出版年度：2019
卷号：306
页码：87-100
DOI：10.4204/EPTCS.306.16
语种：English
出版社：Open Publishing Association
摘要：In this work we design a narrative understanding tool Text2ALM. This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the Text2ALM system was originally outlined by Lierler, Inclezan, and Gelfond (2017) via a manual process of converting a narrative to an ALM model. It relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The effectiveness of system Text2ALM is measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks. We also illustrate that the Text2ALM approach generalizes to a broader spectrum of narratives.