Wikify! Linking Documents to Encyclopedic Knowledge

Wikify! Linking Documents to Encyclopedic Knowledge

Date: November 2007
Creator: Mihalcea, Rada, 1974- & Csomai, Andras
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.
Contributing Partner: UNT College of Engineering
Using Wikipedia for Automatic Word Sense Disambiguation

Using Wikipedia for Automatic Word Sense Disambiguation

Date: April 2007
Creator: Mihalcea, Rada, 1974-
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.
Contributing Partner: UNT College of Engineering
Subjectivity Word Sense Disambiguation

Subjectivity Word Sense Disambiguation

Date: August 2009
Creator: Akkaya, Cem; Wiebe, Janyce M. & Mihalcea, Rada, 1974-
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.
Contributing Partner: UNT College of Engineering
Word Sense Disambiguation with Pattern Learning and Automatic Feature Selection

Word Sense Disambiguation with Pattern Learning and Automatic Feature Selection

Date: December 2002
Creator: Mihalcea, Rada, 1974-
Description: Article discussing word sense disambiguation with pattern learning and automatic feature selection.
Contributing Partner: UNT College of Engineering
Instance Based Learning with Automatic Feature Selection Applied to Word Sense Disambiguation

Instance Based Learning with Automatic Feature Selection Applied to Word Sense Disambiguation

Date: August 2002
Creator: Mihalcea, Rada, 1974-
Description: This paper discusses instance based learning with automatic feature selection applied to word sense disambiguation.
Contributing Partner: UNT College of Engineering
SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

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

Date: 2004
Creator: Mihalcea, Rada, 1974- & Faruque, Ehsanul
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%.
Contributing Partner: UNT College of Engineering
SenseLearner: Word Sense Disambiguation for All Words in Unrestricted Text

SenseLearner: Word Sense Disambiguation for All Words in Unrestricted Text

Date: June 2005
Creator: Mihalcea, Rada, 1974- & Csomai, Andras
Description: This paper describes SenseLearner, a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses.
Contributing Partner: UNT College of Engineering
UNT-Yahoo: SuperSenseLearner: Combining SenseLearner with SuperSense and other Coarse Semantic Features

UNT-Yahoo: SuperSenseLearner: Combining SenseLearner with SuperSense and other Coarse Semantic Features

Date: June 2007
Creator: Mihalcea, Rada, 1974-; Csomai, Andras & Ciaramita, Massimiliano
Description: This paper discusses combining SenseLearner with SuperSence and other coarse semantic features.
Contributing Partner: UNT College of Engineering
Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity

Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity

Date: September 2007
Creator: Sinha, Ravi & Mihalcea, Rada, 1974-
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.
Contributing Partner: UNT College of Engineering
Automatic generation of a coarse grained WordNet

Automatic generation of a coarse grained WordNet

Date: June 2001
Creator: Mihalcea, Rada, 1974- & Moldovan, Dan I.
Description: This paper discusses automatic generation of a coarse grained WordNet.
Contributing Partner: UNT College of Engineering
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