Information extraction tool text2alm : From narratives to action language system descriptions and query answering

Abstract

In this work we design an information extraction tool text2alm capable of narrative understanding with a focus on action verbs. This tool uses an action language  $\mathcal{ALM}$ 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 (In IWCS 2017 – 12th International Conference on Computational Semantics – Short Papers (2017)) via a manual process of converting a narrative to an $\mathcal{ALM}$ model. We refine that theoretical methodology and utilize it in design of the text2alm system. This system relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, (i) knowledge representation and reasoning and (ii) natural language processing. 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. On the path to creating system text2alm, a semantic role labeler text2drs was designed. Its unique feature is the use of the elements of the fine grained linguistic ontology VerbNet as semantic roles/labels in annotating considered text. This paper provides an accurate account on the details behind the text2alm and text2drs systems.