Work

End-to-End Argumentation Mining in Student Essays

Year: 2016

Type: conference-paper

Abstract: Understanding the argumentative structure of a persuasive essay involves addressing two challenging tasks: identifying the components of the essay's argument and identifying the relations that occur between them.We examine the under-investigated task of end-toend argument mining in persuasive student essays, where we (1) present the first results on end-to-end argument mining in student essays using a pipeline approach; (2) address error propagation inherent in the pipeline approach by performing joint inference over the outputs of the tasks in an Integer Linear Programming (ILP) framework; and (3) propose a novel objective function that enables F-score to be maximized directly by an ILP solver.We evaluate our joint-inference approach with our novel objective function on a publiclyavailable corpus of 90 essays, where it yields an 18.5% relative error reduction in F-score over the pipeline system.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.18653/v1/n16-1164

Authors: Isaac Persing, Vincent Ng

Institution: The University of Texas at Dallas

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Awards: RI: Small: Semantics-Based, Weakly-Supervised Coreference Resolution, EAGER: Joint Learning for Knowledge-Rich Coreference Resolution