You limited your search to:

  Partner: UNT College of Engineering
 Collection: UNT Scholarly Works
Self-Configuring Wireless MEMS Network

Self-Configuring Wireless MEMS Network

Date: January 2008
Creator: Akl, Robert G.; Kavi, Krishna M. & El Rewini, Hesham
Description: This presentation discusses miniature, lightweight, self-powered wireless sensors, and networking software needs.
Contributing Partner: UNT College of Engineering
[Review] The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data

[Review] The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data

Date: March 2008
Creator: Mihalcea, Rada, 1974-
Description: This book review discusses 'The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data' by Ronen Feldman and James Sanger. The book is an introduction to text mining, covering the general architecture of text mining systems, along with the main techniques used by such systems.
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
Text Semantic Similarity, with Applications

Text Semantic Similarity, with Applications

Date: September 2005
Creator: Corley, Courtney; Csomai, Andras & Mihalcea, Rada, 1974-
Description: In this paper, the authors present a knowledge-based method for measuring the semantic-similarity of texts. Through experiments performed on two different applications: (1) paraphrase and entailment identification, and (2) word sense similarity, the authors show that this method outperforms the traditional text similarity metrics based on lexical matching.
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
Text Mining for Automatic Image Tagging

Text Mining for Automatic Image Tagging

Date: August 2010
Creator: Leong, Chee Wee; Mihalcea, Rada, 1974- & Hassan, Samer
Description: This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, the authors show that their methods exceed competitive baselines by a large margin, and compare favorably with the state-of-the-art that uses both textual and image features.
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
Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Date: April 29, 2009
Creator: Caragea, Cornelia; Sinapov, Jivko; Dobbs, Drena & Honavar, Vasant
Description: Article discussing models for increasing the reliability of computational methods for identifying functionally important sites from biomolecular sequences.
Contributing Partner: UNT College of Engineering
Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Date: May 10, 2012
Creator: Walia, Rasna R.; Caragea, Cornelia; Lewis, Benjamin A.; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser et al.
Description: Article presenting a review, comparison, and critical assessment of published approaches for predicting RNA-binding residues in proteins using non-redundant databases.
Contributing Partner: UNT College of Engineering
Applications of Logic Flowcharting With a Focus in Autonomous Robotic Operations

Applications of Logic Flowcharting With a Focus in Autonomous Robotic Operations

Date: 2012
Creator: Sink, Ashley Elizabeth; Gscheidle, Karl H.; Namuduri, Kamesh; Li, Li & Sterling, Phillip
Description: This report discusses research on applications of logic flowcharting with a focus in autonomous robotic operations. This research project is part of Research Experiences for Teachers (RET) in Sensor Networks, a National Science Foundation (NSF) funded grant project.
Contributing Partner: UNT College of Engineering