Information Extraction Tool Text2ALM: From Narratives to Action Language System Descriptions

This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the TEXT2 ALM system was originally outlined by Lierler, Inclezan, and Gelfond [13] 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 TEXT2 ALM 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 TEXT2 ALM approach generalizes to a broader spectrum of narratives. 1 Introduction The field of Information Extraction (IE) is concerned with gathering snippets of meaning from text and storing the derived data in structured, machine interpretable form. Consider a sentence BBDO South in Atlanta, which handles corporate advertising for Georgia-Pacific, will assume additional duties for brands like Angel Soft, said Ken Haldin, a spokesman for Georgia-Pacific from Atlanta. A sample IE system that focuses on identifying organizations and their corporate locations may extract the following predicates from this sentence: locatedIn (BBDOSouth, Atlanta) locatedIn (GeorgiaPaci f ic, Atlanta) These predicates can then be stored either in a relational database or a logic program, and queried accordingly by well-known methods in computer science. Thus, IE allows us to turn unstructured data present in text into structured data easily accessible for automated querying. In this paper, we focus on an IE system that is capable of processing simple narratives with action verbs, in particular, verbs that express physical acts such as go, give, and put. Consider a sample narrative that we refer to as the JS discourse: John traveled to the hallway. We appreciate the insights from Michael Gelfond, Daniela Inclezan, Edward Wertz, and Y uanlin Zhang on their work on language ALM, the C OREALML IB library, and system CALM.