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WikiCSSH: Extracting and Evaluating Computer Science Subject Headings from Wikipedia ...
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From user-generated text to insight context-aware measurement of social impacts and interactions using natural language processing
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Impact of Entity Disambiguation Errors on Social Network Properties
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 9 No. 1 (2015): Ninth International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2015)
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A unified framework to identify and extract uncertainty cues, holders, and scopes in one fell-swoop
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Abstract:
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.
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Keyword:
Computational Semantics; Semitic Languages; Social Media Analysis; Uncertainty
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URL: http://hdl.handle.net/2142/78621
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Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization
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Incremental sentiment prediction based on human in the loop learning
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Unsupervised Construction of a Lexicon and a Repository of Variation Patterns for Arabic Modal Multiword Expressions ...
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Uncovering and Managing the Impact of Methodological Choices for the Computational Construction of Socio-Technical Networks from Texts
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In: DTIC (2012)
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Measuring Situational Awareness through Analysis of Communications: A Preliminary Exercise
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In: DTIC (2006)
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Impact of scientific research beyond academia: an alternative classification schema [Online resource]
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IDS-Repository
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Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society [Online resource]
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IDS-Repository
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