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Articles 1 - 5 of 5
Full-Text Articles in Computational Linguistics
Computational Linguistics For Metadata Building: Aggregating Text Processing Technologies For Enhanced Image Access, Judith Klavans, Carolyn Sheffield, Eileen Abels, Joan E. Beaudoin, Laura Jenemann, Jimmy Lin, Tom Lippincott, Rebecca Passonneau, Tandeep Sidhu, Dagobert Soergel, Tae Yano
Computational Linguistics For Metadata Building: Aggregating Text Processing Technologies For Enhanced Image Access, Judith Klavans, Carolyn Sheffield, Eileen Abels, Joan E. Beaudoin, Laura Jenemann, Jimmy Lin, Tom Lippincott, Rebecca Passonneau, Tandeep Sidhu, Dagobert Soergel, Tae Yano
School of Information Sciences Faculty Research Publications
We present a system which applies text mining using computational linguistic techniques to automatically extract, categorize, disambiguate and filter metadata for image access. Candidate subject terms are identified through standard approaches; novel semantic categorization using machine learning and disambiguation using both WordNet and a domain specific thesaurus are applied. The resulting metadata can be manually edited by image catalogers or filtered by semi-automatic rules. We describe the implementation of this workbench created for, and evaluated by, image catalogers. We discuss the system's current functionality, developed under the Computational Linguistics for Metadata Building (CLiMB) research project. The CLiMB Toolkit has been …
Automated Diagnostic Writing Tests: Why? How?, Elena Cotos, Nick Pendar
Automated Diagnostic Writing Tests: Why? How?, Elena Cotos, Nick Pendar
Elena Cotos
Diagnostic language assessment can greatly benefit from a collaborative union of computer-assisted language testing (CALT) and natural language processing (NLP). Currently, most CALT applications mainly allow for inferences about L2 proficiency based on learners’ recognition and comprehension of linguistic input and hardly concern language production (Holland, Maisano, Alderks, & Martin, 1993). NLP is now at a stage where it can be used or adapted for diagnostic testing of learner production skills. This paper explores the viability of NLP techniques for the diagnosis of L2 writing by analyzing the state of the art in current diagnostic language testing, reviewing the existing …
Automatic Identification Of Discourse Moves In Scientific Article Introductions, Elena Cotos, Nick Pendar
Automatic Identification Of Discourse Moves In Scientific Article Introductions, Elena Cotos, Nick Pendar
Elena Cotos
This paper reports on the first stage of building an educational tool for international graduate students to improve their academic writing skills. Taking a text-categorization approach, we experimented with several models to automatically classify sentences in research article introductions into one of three rhetorical moves. The paper begins by situating the project within the larger framework of intelligent computer-assisted language learning. It then presents the details of the study with very encouraging results. The paper then concludes by commenting on how the system may be improved and how the project is intended to be pursued and evaluated.
Referring Expression Generation Challenge 2008 Dit System Descriptions (Dit-Fbi, Dit-Tvas, Dit-Cbsr, Dit-Rbr, Dit-Fbi-Cbsr, Dit-Tvas-Rbr), John D. Kelleher, Brian Mac Namee
Referring Expression Generation Challenge 2008 Dit System Descriptions (Dit-Fbi, Dit-Tvas, Dit-Cbsr, Dit-Rbr, Dit-Fbi-Cbsr, Dit-Tvas-Rbr), John D. Kelleher, Brian Mac Namee
Conference papers
This papers desibes a set of systems developed at DIT for the Referring Expression Generation challenage at INLG 2008.In Proceedings of the 5th International Natural Language Generation Conference (INLG-08)
The Impact Of Directionality In Predications On Text Mining, Gondy Leroy, Marcelo Fiszman, Thomas C. Rindflesch
The Impact Of Directionality In Predications On Text Mining, Gondy Leroy, Marcelo Fiszman, Thomas C. Rindflesch
CGU Faculty Publications and Research
The number of publications in biomedicine is increasing enormously each year. To help researchers digest the information in these documents, text mining tools are being developed that present co-occurrence relations between concepts. Statistical measures are used to mine interesting subsets of relations. We demonstrate how directionality of these relations affects interestingness. Support and confidence, simple data mining statistics, are used as proxies for interestingness metrics. We first built a test bed of 126,404 directional relations extracted from biomedical abstracts, which we represent as graphs containing a central starting concept and 2 rings of associated relations. We manipulated directionality in four …