RI: Small: Adapting a Natural Logic Reasoning Platform to the Task of Entailment Inference

目前的人工智能系统仍然缺乏处理语言语义微妙以及人类话语和思维的主题广度所需的知识和推理能力。这个项目正在开发一个词汇和其他一般知识的基本曲目，用于一个强大的推理引擎（EPILOG），该引擎专门设计用于支持无限制的语言理解和推理。所采用的方法利用了近年来在“自然逻辑”领域中对基于语言的推理的见解，系统地利用了语言的词级和结构蕴涵属性。这些很容易在EPILOG中建模，它使用类似语言的意义表示（情景逻辑）。一些一般的语义属性正在被手工编码，此外，大量的知识项目正在从词汇资源（如WordNet和vernet）以及从大型文本语料库派生的词相似或释义聚类中被计算提取。预期的结果是一个基本词汇和其他常识性知识的知识库，它将允许演示许多以前不可行的基于语言的推理，包括向前和向后推理以及现有测试套件中的许多多前提蕴涵推理。这将显著提高基本语言理解和“明显推理”机械化的技术水平，潜在应用于基于对话的智能代理（用于问答、辅导、个人协助等），以及通过机器阅读进行知识引导。研究结果将通过会议和期刊上的论文以及通过提供EPILOG和新开发的知识库的网站进行传播。

Current AI systems still lack the knowledge and reasoning abilities needed to handle the semantic subtleties of language and the thematic breadth of human discourse and thinking.This project is developing a basic repertoire of lexical andother general knowledge for use in a powerful inference engine (EPILOG) designed expressly to support unrestricted language understanding and reasoning.The methods being employed exploit the insights into language-basedinference gained in recent years in the area of "natural Logic",which makes systematic use of word-level and structural entailmentproperties of language. These are easily modeled in EPILOG, whichuses a language-like meaning representation (Episodic Logic). Somevery general semantic properties are being manually encoded, and in addition, large numbers of knowledge items are being extractedcomputationally from lexical resources such as WordNet and VerbNet,and from word similarity or paraphrase clusters derived from large text corpora.The expected result is a knowledge base of fundamental lexical andother commonsense knowledge that will allow demonstration of manypreviously infeasible language-based inferences, including both forward and backward reasoning and many multi-premise entailment inferences in existing test suites. This will significantly advance the state of the art in basic language understanding and in mechanizing "obvious inferences", with potential applications to intelligent dialogue-based agents (for question answering, tutoring, personal assistance, etc.), and to knowledge bootstrapping through machine reading. The results will be disseminated both through papers at conferences and in journals, and through web sites making available EPILOG and the newly developed knowledge bases.