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A Minimally Supervised Word Sense Disambiguation Algorithm Using Syntactic Dependencies and Semantic Generalizations

Description: Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the… more
Date: December 2005
Creator: Faruque, Md. Ehsanul
Item Type: Refine your search to only Thesis or Dissertation
Partner: UNT Libraries
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Using Wikipedia for Automatic Word Sense Disambiguation

Description: This paper describes a method for generating sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, the authors show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers.
Date: April 2007
Creator: Mihalcea, Rada, 1974-
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
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Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity

Description: This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.
Date: September 2007
Creator: Sinha, Ravi & Mihalcea, Rada, 1974-
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
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Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation

Description: In this paper, the authors discuss research on whether they can use Mechanical Turk (MTurk) to acquire good annotations with respect to gold-standard data, whether they can filter out low-quality workers (spammers), and whether there is a learning effect associated with repeatedly completing the same kind of task.
Date: June 2010
Creator: Akkaya, Cem; Conrad, Alexander; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
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The SENSEVAL-3 English Lexical Sample Task

Description: This paper presents the task definition, resources, participating systems, and comparative results for the English lexical sample task, which was organized as part of the SENSEVAL-3 evaluation exercise.
Date: July 2004
Creator: Mihalcea, Rada, 1974-; Chklovski, Timothy A. (Timothy Anatolievich), 1977- & Kilgarriff, Adam
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
open access

SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

Description: This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambiguate all content words in a text using the sense from WordNet. SenseLearner participated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%.
Date: 2004
Creator: Mihalcea, Rada, 1974- & Faruque, Ehsanul
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
open access

Wikify! Linking Documents to Encyclopedic Knowledge

Description: This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks.
Date: November 2007
Creator: Mihalcea, Rada, 1974- & Csomai, Andras
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
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Co-training and Self-training for Word Sense Disambiguation

Description: This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.
Date: May 2004
Creator: Mihalcea, Rada, 1974-
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
open access

Subjectivity Word Sense Disambiguation

Description: This paper investigates a new task, subjectivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses.
Date: August 2009
Creator: Akkaya, Cem; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
Item Type: Refine your search to only Paper
Partner: UNT College of Engineering
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