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TextRank: Bringing Order into Texts

TextRank: Bringing Order into Texts

Date: July 2004
Creator: Mihalcea, Rada, 1974- & Tarau, Paul
Description: In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.
Contributing Partner: UNT College of Engineering
Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads

Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads

Date: October 2012
Creator: Biyani, Prakhar; Bhatia, Sumit; Caragea, Cornelia & Mitra, Prasenjit
Description: Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.
Contributing Partner: UNT College of Engineering
Throughput Optimization in Multi-Cell CDMA Networks

Throughput Optimization in Multi-Cell CDMA Networks

Date: March 2005
Creator: Akl, Robert G.; Naraghi-Pour, Mort & Hegde, Manju V.
Description: In this paper, the authors investigate the performance of a multi-cell CDMA network by determining the maximum throughput that the network can archive for a given grade-of-service requirement, quality-of-service requirement, network topology and call arrival rate profile.
Contributing Partner: UNT College of Engineering
Throughput Validation of an Advanced Channel Assignment Algorithm in IEEE 802.11 WLAN

Throughput Validation of an Advanced Channel Assignment Algorithm in IEEE 802.11 WLAN

Date: February 2009
Creator: Haidar, Mohamad; Al-Rizzo, Hussain Mudhaffar Younis, 1957-; Chan, Yupo; Akl, Robert G. & Bouharras, Mohamad
Description: In this article, an enhanced channel-assignment algorithm at the Access Points (APs) of a Wireless Local Area Network (WLAN) is validated.
Contributing Partner: UNT College of Engineering
Topic Identification Using Wikipedia Graph Centrality

Topic Identification Using Wikipedia Graph Centrality

Date: May 2009
Creator: Coursey, Kino High & Mihalcea, Rada, 1974-
Description: This paper presents a method for automatic topic identification using a graph-centrality algorithm applied to an encyclopedic graph derived from Wikipedia. When tested on a data set with manually assigned topics, the system is found to significantly improve over a simpler baseline that does not make use of the external encyclopedic knowledge.
Contributing Partner: UNT College of Engineering
Toward Communicating Simple Sentences Using Pictorial Representations

Toward Communicating Simple Sentences Using Pictorial Representations

Date: April 2009
Creator: Mihalcea, Rada, 1974- & Leong, Ben
Description: This article discusses communicating simple sentences using pictorial representations.
Contributing Partner: UNT College of Engineering
Transformational Paradigm for Engineering and Engineering Technology Education

Transformational Paradigm for Engineering and Engineering Technology Education

Date: November 2008
Creator: Barbieri, Enrique & Fitzgibbon, William
Description: This paper discusses a transformational paradigm for engineering and engineering technology education at the baccalaureate level.
Contributing Partner: UNT College of Engineering
UMTS Capacity and Throughput Maximization for Different Spreading Factors

UMTS Capacity and Throughput Maximization for Different Spreading Factors

Date: July 2006
Creator: Akl, Robert G. & Nguyen, Son
Description: This article discusses UMTS capacity and throughput maximization for different spreading factors.
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
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