Search Results

Networks and Natural Language Processing

Description: Article discussing networks and natural language processing. The authors present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.
Date: September 2008
Creator: Radev, Dragomir R. & Mihalcea, Rada, 1974-
Item Type: Article
Partner: UNT College of Engineering

Learning to Identify Emotions in Text

Description: This paper discusses learning to identify emotions in text.
Date: March 2008
Creator: Strapparava, Carlo, 1962- & Mihalcea, Rada, 1974-
Item Type: Paper
Partner: UNT College of Engineering

UNT 2005 TREC QA Participation: Using Lemur as IR Search Engine

Description: This paper reports the authors' TREC 2005 QA participation. The authors' QA system Eagle QA developed last year was expanded and modified for this year's QA experiments. Particularly, the authors used Lemur 4.1 as the Information Retrieval (IR) Engine this year to find documents that may contain answers for the test questions from the document collection. The authors' result shows Lemur did a reasonable job on finding relevant documents. But certainly there is room for further improvement.
Date: 2005
Creator: Chen, Jiangping; Yu, Ping & Ge, He
Item Type: Paper
Partner: UNT College of Information

Texas Newspapers Natural Language Processing

Description: This dataset includes data on natural language processing from the Texas Newspapers Project. The dataset includes word counts, name entity recognition results, and topic models.
Date: April 7, 2013
Creator: Torget, Andrew J., 1978-
Item Type: Dataset
Partner: UNT Libraries

Text Semantic Similarity, with Applications

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.
Date: September 2005
Creator: Corley, Courtney; Csomai, Andras & Mihalcea, Rada, 1974-
Item Type: Paper
Partner: UNT College of Engineering

SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

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%.
Date: 2004
Creator: Mihalcea, Rada, 1974- & Faruque, Ehsanul
Item Type: Paper
Partner: UNT College of Engineering

Parallel Texts

Description: Article discussing parallel texts and natural language processing.
Date: September 2005
Creator: Mihalcea, Rada, 1974- & Simard, Michel
Item Type: Article
Partner: UNT College of Engineering

Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling

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.
Date: October 2005
Creator: Mihalcea, Rada, 1974-
Item Type: Paper
Partner: UNT College of Engineering

Finding Semantic Associations on Express Lane

Description: This paper introduces a new codification scheme for efficient computation of measures in semantic networks. The scheme is particularly useful for fast computation of semantic associations between words and implementation of an informational retrieval operator for efficient search in semantic spaces. Other applications may also be possible.
Date: May 2004
Creator: Nastase, Vivi & Mihalcea, Rada, 1974-
Item Type: Paper
Partner: UNT College of Engineering

Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization

Description: Abstract: This paper presents an innovative unsupervised method for automatic sentence extraction using graph-based ranking algorithms. We evaluate the method in the context of a text summarization task, and show that the results obtained compare favorably with previously published results on established benchmarks.
Date: July 2004
Creator: Mihalcea, Rada, 1974-
Item Type: Paper
Partner: UNT College of Engineering