Identifying Argumentation Schemes in Genetics Research Articles

Proceedings of the 2nd Workshop on Argumentation Mining, pages 12–21, Denver, Colorado, June 4, 2015. c⃝2015 Association for Computational Linguistics Identifying Argumentation Schemes in Genetics Research Articles Nancy L. Green Dept. of Computer Science U. of N. Carolina Greensboro Greensboro, NC 27402, USA nlgreen@uncg.edu Abstract This paper presents preliminary work on identification of argumentation schemes, i.e., identifying premises, conclusion and name of argumentation scheme, in argu- ments for scientific claims in genetics re- search articles. The goal is to develop annotation guidelines for creating corpo- ra for argumentation mining research. This paper gives the specification of ten semantically distinct argumentation schemes based on analysis of argumenta- tion in several journal articles. In addi- tion, it presents an empirical study on readers’ ability to recognize some of the argumentation schemes. 1 Introduction There has been an explosion in the on-line publi- cation of genetics research articles, creating a critical need for information access tools for ge- netics researchers, biological database curators, and clinicians. Research on biologi- cal/biomedical natural language processing (Bi- oNLP) is an active area of research with the goal of developing such tools. Previous research in BioNLP has focused mainly on fundamental text mining challenges such as named entity recogni- tion and relation extraction (Cohen and Demner- Fushman 2014). However, a key feature of scien- tific writing is the use of argumentation. It is important for information access tools to recognize argumentation in scientific text. First, argumentation is a level of discourse analysis that provides critical context for interpretation of a text. For example, a text may give an argument against a hypothesis P, so it would be misleading for a text mining program to extract P as a fact stated in that text. Second, a user should be able to access a summary of arguments for and against a particular claim. Also, to evaluate the strength of an argument a user should be able to see the arguments upon which it depends, i.e., arguments supporting or attacking its premises. Third, tools that display citation relationships among documents (Teufel 2010) could provide finer-grained information about relationships between arguments in different documents. Argumentation mining aims to automatically identify arguments in text, the arguments’ prem- ises, conclusion and argumentation scheme (or form of argument), and relationships between arguments in a text or set of texts. Most previous work in argumentation mining has focused on non-scientific text (e.g. Mochales and Moens 2011; Feng and Hirst 2011; Cabrio and Villata 2012). Previous NLP research on scientific dis- course (e.g. Mizuta et al. 2005; Teufel 2010; Liakata 2012a) has focused on recognizing in- formation status (hypothesis, background knowledge, new knowledge claim, etc.) but has not addressed deeper argumentation analysis. This paper presents our preliminary work on identification of argumentation schemes in ge- netics research articles. We define this subtask, unlike some others, e.g. Feng and Hirst (2011), as identifying the premises and conclusion of an argument together with the name of its argumen- tation scheme. One contribution of the paper is the specification of …

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