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Hits 1 – 20 of 46

1	08-0117-08 is gratefully acknowledged. Taming Social Tags: Computational Linguistic Analysis of Tags for Images in Museums
	Judith L. Klavans; Raul Guerra; Rebecca Laplante...
	In: http://lampsrv02.umiacs.umd.edu/pubs/TechReports/LAMP_155/LAMP_155.pdf (2011)
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2	Similarity-based Multilingual Multi-Document Summarization
	David Kirk Evans; Kathleen Mckeown; Judith L. Klavans
	In: http://www.cs.columbia.edu/nlp/papers/2005/evans_al_05.pdf (2005)
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3	Inducing constraint-based grammars from a small semantic treebank
	Smaranda Muresan; Tudor Muresan; Judith L. Klavans
	In: http://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-05/SS04-05-009.pdf (2004)
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4	Inducing constraint-based grammars from a small semantic treebank
	Smaranda Muresan; Tudor Muresan; Judith L. Klavans
	In: http://www.cs.columbia.edu/nlp/papers/2004/muresan_al_04.pdf (2004)
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5	Columbia newsblaster: multilingual news summarization on the web
	David Kirk Evans; Judith L. Klavans
	In: http://www.cs.columbia.edu/%7Edevans/papers/mlNewsblaster.pdf (2004)
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6	A Platform for Multilingual News Summarization Paper ID: Multilingual-Summarization-CU
	David Kirk Evans; Judith L. Klavans
	In: http://www.cs.columbia.edu/techreports/cucs-014-03.pdf (2003)
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7	A Platform for Multilingual . . .
	David Kirk Evans; Judith L. Klavans
	In: http://www.cs.columbia.edu/~library/TR-repository/reports/reports-2003/cucs-014-03.ps.gz (2003)
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8	Methods for precise named entity matching in digital collections
	Peter T. Davis; David K. Elson; Judith L. Klavans
	In: http://www.cs.columbia.edu/nlp/papers/2003/davis_al_03.pdf (2003)
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9	Methods for precise named entity matching in digital collections
	Peter T. Davis; David K. Elson; Judith L. Klavans
	In: http://www.cs.columbia.edu/~delson/pubs/jcdl03.pdf (2003)
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10	B.: Tackling the internet glossary glut: Automatic extraction and evaluation of genus phrases
	Judith L. Klavans
	In: http://www.cs.columbia.edu/nlp/papers/2003/klavans_al_03.pdf (2003)
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11	Combining Linguistic and Machine Learning Techniques for Email
	Smaranda Muresan; Evelyne Tzoukermann; Judith L. Klavans
	In: http://cnts.uia.ac.be/conll2001/ps/15259mur.ps (2001)
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12	Document Processing with LinkIT
	David K. Evans; Judith L. Klavans; Nina Wacholder
	In: http://133.23.229.11/~ysuzuki/Proceedingsall/RIAO2000/Friday/110DP1.pdf (2000)
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13	Document Processing with LinkIT
	David Evans Judith; David K. Evans; Judith L. Klavans...
	In: http://www.columbia.edu/cu/cria/Publications/riao2000.ps (2000)
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14	Evaluation of computational linguistic techniques for identifying significant topics for browsing applications
	Judith L. Klavans; Nina Wacholder; David K. Evans
	In: http://www.lrec-conf.org/proceedings/lrec2000/pdf/355.pdf (2000)
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15	The Eurospider Retrieval System and the TREC-8 Cross-Language Track
	Martin Braschler; Min-yen Kan; Peter Schäuble...
	In: http://www.cs.columbia.edu/~min/papers/eit_t8f.pdf (1999)
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16	The Eurospider Retrieval System and the TREC-8 Cross-Language Track
	Martin Braschler; Min-yen Kan; Peter Schäuble...
	In: http://trec.nist.gov/pubs/trec8/papers/eit_t8f.pdf (1999)
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17	Towards Multidocument Summarization by Reformulation: Progress and Prospects
	Kathleen R. McKeown; Judith L. Klavans; Vasileios Hatzivassiloglou...
	In: http://www.cs.columbia.edu/nlp/newsblaster/papers/stim2.pdf (1999)
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18	Towards multidocument summarization by reformulation: Progress and prospects
	Kathleen R. Mckeown; Judith L. Klavans; Vasileios Hatzivassiloglou; Regina Barzilay; Eleazar Eskin
	In: http://www.cs.columbia.edu/~vh/Papers/1999/Summarization-AAAI.pdf (1999)
	Abstract: By synthesizing information common to retrieved documents, multi-document summarization can help users of information retrieval systems to find relevant documents with a minimal amount of reading. We are developing a multidocument summarization system to automatically generate a concise summary by identifying and synthesizing similarities across a set of related documents. Our approach is unique in its integration of machine learning and statistical techniques to identify similar paragraphs, intersection of similar phrases within paragraphs, and language generation to reformulate the wording of the summary. Our evaluation of system components shows that learning over multiple extracted linguistic features is more effective than information retrieval approaches at identifying similar text units for summarization and that it is possible to generate a fluent summary that conveys similarities among documents even when full semantic interpretations of the input text are not available.
	URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.8880 http://www.cs.columbia.edu/~vh/Papers/1999/Summarization-AAAI.pdf
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19	Detecting text similarity over short passages: exploring linguistic feature combinations via machine learning
	Vasileios Hatzivassiloglou; Judith L. Klavans; Eleazar Eskin
	In: http://www.cs.columbia.edu/~vh/Papers/1999/SimFinder-EMNLP.pdf (1999)
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20	Toward Multi-document summarization by Reformulation: progress
	Kathleen R. Mckeown; Judith L. Klavans; Vasileios Hatzivassiloglou...
	In: https://www.aaai.org/Papers/AAAI/1999/AAAI99-065.pdf (1999)
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