Linguistic Ethnography: Identifying Dominant Word Classes in Text

Linguistic Ethnography: Identifying Dominant Word Classes in Text

Date: March 2009
Creator: Pulman, Stephen & Mihalcea, Rada, 1974-
Description: This paper discusses linguistic ethnography.
Contributing Partner: UNT College of Engineering
Building Multilingual Semantic Networks with Non-Expert Contributions over the Web

Building Multilingual Semantic Networks with Non-Expert Contributions over the Web

Date: November 2003
Creator: Ayewah, Nathanial; Mihalcea, Rada, 1974- & Nastase, Vivi
Description: This paper discusses building multilingual semantic networks.
Contributing Partner: UNT College of Engineering
Corpus-based and Knowledge-based Measures of Text Semantic Similarity

Corpus-based and Knowledge-based Measures of Text Semantic Similarity

Date: July 2006
Creator: Mihalcea, Rada, 1974-; Corley, Courtney & Strapparava, Carlo, 1962-
Description: This article discusses corpus-based and knowledge-based measures of text semantic similarity.
Contributing Partner: UNT College of Engineering
Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing

Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing

Date: 2005
Creator: Shi, Lei & Mihalcea, Rada, 1974-
Description: This paper describes the authors' work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing.
Contributing Partner: UNT College of Engineering
Measuring the Semantic Similarity of Texts

Measuring the Semantic Similarity of Texts

Date: June 2005
Creator: Corley, Courtney & Mihalcea, Rada, 1974-
Description: This paper discusses measuring the semantic similarity of texts.
Contributing Partner: UNT College of Engineering
SemEval-2007 Task 14: Affective Text

SemEval-2007 Task 14: Affective Text

Date: June 2007
Creator: Strapparava, Carlo, 1962- & Mihalcea, Rada, 1974-
Description: The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, the authors describe the data set used in the evaluation and the results obtained by the participating systems.
Contributing Partner: UNT College of Engineering