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
A Method for Word Sense Disambiguation of Unrestricted Text

A Method for Word Sense Disambiguation of Unrestricted Text

Date: June 1999
Creator: Mihalcea, Rada, 1974- & Moldovan, Dan I.
Description: This paper discusses a method for word sense disambiguation of unrestricted text.
Contributing Partner: UNT College of Engineering
Making Sense Out of the Web

Making Sense Out of the Web

Date: November 2004
Creator: Mihalcea, Rada, 1974-
Description: This paper discusses the main lines of research in deriving efficient Word Sense Disambiguation.
Contributing Partner: UNT College of Engineering
Method, System and Apparatus for Automatic Keyword Extraction

Method, System and Apparatus for Automatic Keyword Extraction

Date: November 6, 2009
Creator: Csomai, Andras & Mihalcea, Rada, 1974-
Description: Patent relating to a method, system and apparatus for automatic keyword extraction.
Contributing Partner: UNT College of Engineering
An Automatic Method for Generating Sense Tagged Corpora

An Automatic Method for Generating Sense Tagged Corpora

Date: 1999
Creator: Mihalcea, Rada, 1974- & Moldovan, Dan I.
Description: This paper discusses an automatic method for generating sense tagged corpora.
Contributing Partner: UNT College of Engineering
An Iterative Approach to Word Sense Disambiguation

An Iterative Approach to Word Sense Disambiguation

Date: May 2000
Creator: Mihalcea, Rada, 1974- & Moldovan, Dan I.
Description: This paper discusses an iterative approach to Word Sense Disambiguation.
Contributing Partner: UNT College of Engineering
Word Sense Disambiguation based on Semantic Density

Word Sense Disambiguation based on Semantic Density

Date: August 1998
Creator: Mihalcea, Rada, 1974- & Moldovan, Dan I.
Description: This article discusses word sense disambiguation based on semantic density.
Contributing Partner: UNT College of Engineering
Open Mind Word Expert: Creating Large Data Collections with Web Users' Help

Open Mind Word Expert: Creating Large Data Collections with Web Users' Help

Date: June 2002
Creator: Chklovski, Timothy A. (Timothy Anatolievich), 1977- & Mihalcea, Rada, 1974-
Description: This article discusses Open Mind Word Expert (OMWE), a system that aims to tap people's ability to disambiguate words and to give computers the benefit of people's knowledge.
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
A Minimally Supervised Word Sense Disambiguation Algorithm Using Syntactic Dependencies and Semantic Generalizations

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

Date: December 2005
Creator: Faruque, Md. Ehsanul
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 method proposed in this thesis attempts to address all words in unrestricted text. The method is based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. The method was tested on standard benchmarks as used during the SENSEVAL-2 and SENSEVAL-3 WSD international evaluation exercises, and was found to be competitive.
Contributing Partner: UNT Libraries
Multilingual Word Sense Disambiguation Using Wikipedia

Multilingual Word Sense Disambiguation Using Wikipedia

Date: August 2013
Creator: Dandala, Bharath
Description: Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. ...
Contributing Partner: UNT Libraries
Improving the Search on the Internet by Using WordNet and Lexical Operators

Improving the Search on the Internet by Using WordNet and Lexical Operators

Date: July 21, 1999
Creator: Moldovan, Dan I. & Mihalcea, Rada, 1974-
Description: This article discusses improving the search on the internet by using WordNet and lexical operators.
Contributing Partner: UNT College of Engineering