A unified framework to identify and extract uncertainty cues, holders, and scopes in one fell-swoop

Uncertainty refers to the language aspects that express hypotheses and speculations where propositions are held as (un)certain, (im)probable, or (im)possible. Automatic uncertainty analysis is crucial for several Natural Language Processing (NLP) applications that need to distinguish between factual (i.e. certain) and nonfactual (i.e. negated or uncertain) information. Typically, a comprehensive automatic uncertainty analyzer has three machine learning models for uncertainty detection, attribution, and scope extraction. To-date, and to the best of my knowledge, current research on uncertainty automatic analysis has only focused on uncertainty attribution and scope extraction, and has typically tackled each task with a different machine learning approach. Furthermore, current research on uncertainty automatic analysis has been restricted to specific languages, particularly English, and to specific linguistic genres, including biomedical and newswire texts, Wikipedia articles, and product reviews. In this research project, I attempt to address the aforementioned limitations of current research on automatic uncertainty analysis. First, I develop a machine learning model for uncertainty attribution, the task typically neglected in automatic uncertainty analysis. Second, I propose a unified framework to identify and extract uncertainty cues, holders, and scopes in one-fell swoop by casting each task as a supervised token sequence labeling problem. Third, I choose to work on the Arabic language, in contrast to English, the most commonly studied language in the literature of automatic uncertainty analysis. Finally, I work on the understudied linguistic genre of tweets. This research project results in a novel NLP tool, i.e., a comprehensive automatic uncertainty analyzer for Arabic tweets, with a practical impact on NLP applications that rely on uncertainty automatic analysis. The tool yields an F1 score of 0.759, averaged across its three machine learning models. Furthermore, through this research, the research community and I gain insights into (1) the challenges presented by Arabic as an agglutinative morphologically-rich language with a flexible word order, in contrast to English; (2) the challenges of the linguistic genre of tweets for uncertainty automatic analysis; and (3) the type of challenges that my proposed unified framework successfully addresses and boosts performance for.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Linguistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Al-Sabbagh, Rania Mostafa

Contributors dc:contributor

• Girju, Roxana
• Diesner, Jana
• Benmamoun, Elabbas
• Hockenmaier , John

• Computational Semantics
• Semitic Languages
• Uncertainty
• Social Media Analysis

Statement dc:rights

• Copyright 2015 Rania Mostafa Al-Sabbagh

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/78621

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/78621