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.

Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.

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.

In this article, an enhanced channel-assignment algorithm at the Access Points (APs) of a Wireless Local Area Network (WLAN) is validated.

This paper demonstrates that the greatest value for annotation lies in single annotating more data.

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.

Paper for the 2011 ACL Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. This paper discusses topic modeling on historical newspaper.

This paper shows results from an investigation whether a classifier can be taught to identify these constructions and consideration of the hypothesis that identifying construction types can improve the semantic interpretation of previously unseen predicate uses.

Paper for the 2014 Association for Information Science and Technology (ASIS&T) Annual Meeting. This paper discusses building a collection of web archiving research articles.

This paper proposes a new shared task on grading student answers with the goal of enabling well-targeted and flexible feedback in a tutorial dialogue setting.

This paper presents a two-step procedure to extract positive meaning from verbal negation.

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.

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.

This paper describes the University of North Texas SubFinder system. The system is able to provide the most likely set of substitutes for a word in a given context, by combining several techniques and knowledge sources. SubFinder has successfully participated in the best and out of ten (oot) tracks in the SEMEVAL lexical substitution task, consistently ranking in the first or second place.

This paper discusses combining SenseLearner with SuperSence and other coarse semantic features.

This paper presents a method for automatic topic identification using an encyclopedic graph derived from Wikipedia. The system is found to exceed the performance of previously proposed machine learning algorithms for topic identification, with an annotation consistency comparable to human annotations.

This article discusses using the essence of texts to improve document classification.

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.

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.

This paper presents the task definition, resources, participating systems, and comparative results for the shared task on word alignment which was organized as part of the Association for Computational Linguistics (ACL) 2005 Workshop on Building and Using Parallel Texts. The shared task included English-Inuktitut, Romanian-English, and English-Hindi sub-tasks, and drew the participation of ten teams from around the world with a total of 50 systems.

This paper discusses word sense and subjectivity.

This article discusses word sense disambiguation based on semantic density.

This paper discusses a WordNet-based interface to Internet search engines.