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Claude de Loupy

dblp:06/6555 · DBLP profile ↗
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7ranked-venue papers
3as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 3 first-authorDatabases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 87% Query processing and optimization · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › interactive information retrieval
browsing
0.012004
Natural language processing for browse help · SIGIR 2004
Information retrieval
interactive information retrieval
0.012004
Natural language processing for browse help · SIGIR 2004
Query processing and optimization
search space reduction
0.012004
Natural language processing for browse help · SIGIR 2004

Methods — techniques the papers use, named apart from their topics

named entity recognition · 0.0
YearPublicationVenuePosition
2014 Thematic Cohesion: measuring terms discriminatory power toward themes
Clément de Groc, Xavier Tannier, Claude de Loupy
LREC3
2010 OAL: A NLP Architecture to Improve the Development of Linguistic Resources for NLP
Javier Couto, Helena Blancafort, Somara Seng, Nicolas Kuchmann, Anass Talby, Claude de Loupy
LREC6
2010 A French Human Reference Corpus for Multi-Document Summarization and Sentence Compression
Claude de Loupy, Marie Guégan, Christelle Ayache, Somara Seng, Juan-Manuel Torres-Moreno
LREC1
2004 Browsing Help for a Faster Retrieval
Eric Crestan, Claude de Loupy
COLING2
2004 Natural language processing for browse help
abstract
In this paper, we will present three "browsing" systems that should save user's time. The first uses named entities and gives a way to reduce search space. By using a information visualization system, the user can comprehend more easily the content of a corpus or a document. Named entities are highlighted for quick reading, temporal and geographic representation gives a global view of the result of a query. All these browse and search helps seem to be very useful. Nevertheless, an evaluation would give more practical results.
Eric Crestan, Claude de Loupy
SIGIR2
2000 Using Few Clues Can Compensate the Small Amount of Resources Available for Word Sense Disambiguation
Claude de Loupy, Marc El-Bèze
LREC1
1998 Word sense disambiguation using HMM tagger
Claude de Loupy, Pierre-François Marteau
LREC1