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). The World Wide Web has both exacerbated the need and provided an opportunity for creating automatic tools for language processing. OMWE is a system that aims to tap people's ability to disambiguate words and to give computers the benefit of people's knowledge. Any Web user can visit the OMWE site and contribute some knowledge about the meanings of given words in given sentences. As a result, OMWE creates large sense-tagged corpora that can be used to build automatic WSD systems.
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: This paper presents a novel approach for word sense disambiguation. The underlying algorithm has two main components: (1) pattern learning from available sense-tagged corpora (SemCor), from dictionary definitions (WordNet) and from a generated corpus (GenCor), and (2) instance based learning with automatic feature selection, when training data is available for a particular word. The ideas described in this paper were implemented in a system that achieved the best score during the SENSEVAL-2 evaluation exercise, for both English all words and English lexical sample tasks.
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. The paper also shows how the two methods can be combined into a system able to automatically enrich a text with links to encyclopedic knowledge. Given an input document, the system identifies the important concepts in the text and automatically links these concepts to the corresponding Wikipedia pages. Evaluations of the system show that the automatic annotations are reliable and hardly distinguishable from manual annotations.
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
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
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
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
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
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
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
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