Extrapolating Subjectivity Research to Other Languages

Extrapolating Subjectivity Research to Other Languages

Date: May 2013
Creator: Banea, Carmen
Description: Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the ...
Contributing Partner: UNT Libraries
Random-Walk Term Weighting for Improved Text Classification

Random-Walk Term Weighting for Improved Text Classification

Date: September 2007
Creator: Hassan, Samer; Mihalcea, Rada, 1974- & Banea, Carmen
Description: This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier.
Contributing Partner: UNT College of Engineering
A Bootstrapping Method for Building Subjectivity Lexicons for Languages with Scarce Resources

A Bootstrapping Method for Building Subjectivity Lexicons for Languages with Scarce Resources

Date: May 2008
Creator: Banea, Carmen; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
Description: This article discusses a bootstrapping method for building subjectivity lexicons for languages with scarce resources.
Contributing Partner: UNT College of Engineering
Multilingual Subjectivity Analysis Using Machine Translation

Multilingual Subjectivity Analysis Using Machine Translation

Date: October 2008
Creator: Banea, Carmen; Mihalcea, Rada, 1974-; Wiebe, Janyce M. & Hassan, Samer
Description: This paper discusses multilingual subjectivity analysis using machine translation.
Contributing Partner: UNT College of Engineering
Multilingual Subjectivity: Are More Languages Better?

Multilingual Subjectivity: Are More Languages Better?

Date: August 2010
Creator: Banea, Carmen; Mihalcea, Rada, 1974- & Wiebe, Janyce M.
Description: This paper discusses multilingual subjectivity.
Contributing Partner: UNT College of Engineering
Learning Multilingual Subjective Language via Cross-Lingual Projections

Learning Multilingual Subjective Language via Cross-Lingual Projections

Date: June 2007
Creator: Mihalcea, Rada, 1974-; Banea, Carmen & Wiebe, Janyce M.
Description: This paper discusses learning multilingual subjective language via cross-lingual projections.
Contributing Partner: UNT College of Engineering
UNT: SubFinder: Combining Knowledge Sources for Automatic Lexical Substitution

UNT: SubFinder: Combining Knowledge Sources for Automatic Lexical Substitution

Date: June 2007
Creator: Hassan, Samer; Csomai, Andras; Banea, Carmen; Sinha, Ravi & Mihalcea, Rada, 1974-
Description: This paper describes the University of North Texas SubFinder system. The system is able to provide the most likely set of substitutes for a word in a given context, by combining several techniques and knowledge sources. SubFinder has successfully participated in the best and out of ten (oot) tracks in the SEMEVAL lexical substitution task, consistently ranking in the first or second place.
Contributing Partner: UNT College of Engineering