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The Role of Non-Ambiguous Words in Natural Language Disambiguation

The Role of Non-Ambiguous Words in Natural Language Disambiguation

Date: September 2003
Creator: Mihalcea, Rada, 1974-
Description: This article discusses the role of non-ambiguous words in natural language disambiguation.
Contributing Partner: UNT College of Engineering
The Semantic Wildcard

The Semantic Wildcard

Date: May 2002
Creator: Mihalcea, Rada, 1974-
Description: This paper introduces the semantic wildcard, one of the most powerful operators implemented in IRSLO, which allows for searches along general-specific lines.
Contributing Partner: UNT College of Engineering
A Semi-Complete Disambiguation Algorithm for Open Text

A Semi-Complete Disambiguation Algorithm for Open Text

Date: 2000
Creator: Mihalcea, Rada, 1974-
Description: This paper discusses a semi-complete disambiguation algorithm for open text.
Contributing Partner: UNT College of Engineering
Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling

Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling

Date: October 2005
Creator: Mihalcea, Rada, 1974-
Description: This paper introduces a graph-based algorithm for sequence data labeling, using random walks on graphs encoding label dependencies. The algorithm is illustrated and tested in the context of an unsupervised word sense disambiguation problem, and shown to significantly outperform the accuracy achieved through individual label assignment, as measured on standard sense-annotated data sets.
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
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
Creating Large Annotated Data Collections with Web Users' Help

Creating Large Annotated Data Collections with Web Users' Help

Date: April 2003
Creator: Mihalcea, Rada, 1974- & Chklovski, Timothy A. (Timothy Anatolievich), 1977-
Description: This paper discusses creating annotated data collections.
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
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
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