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Emmanuel Prochasson

dblp:56/764 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2011
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

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.

Artificial intelligence
1 paper
Machine translation · 87% Information extraction and text analysis · 13%

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

TopicWeightPapersLastEvidence papers
Natural language and speech › Machine translation
bilingual lexicon induction
0.112011
Rare Word Translation Extraction from Aligned Comparable Documents · ACL 2011
Natural language and speech › Machine translation › word translation
low-frequency word translation
0.112011
Rare Word Translation Extraction from Aligned Comparable Documents · ACL 2011
Natural language and speech › Information extraction and text analysis › corpus linguistics
comparable corpora mining
0.012011
Rare Word Translation Extraction from Aligned Comparable Documents · ACL 2011

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

transliteration mining · 0.1comparable document alignment · 0.1
YearPublicationVenuePosition
2011 Rare Word Translation Extraction from Aligned Comparable Documents
Emmanuel Prochasson, Pascale Fung
ACL1
2011 Mining Parallel Documents Using Low Bandwidth and High Precision CLIR from the Heterogeneous Web
Simon Shi, Pascale Fung, Emmanuel Prochasson, Chi-kiu Lo, Dekai Wu
IJCNLP3
2007 Language Models for Handwritten Short Message Services
abstract
Handwriting is an alternative method for entering texts composing Short Message Services. However, a whole new language features the texts which are produced. They include for instance abbreviations and other consonantal writing which sprung up for time saving and fashion. We have collected and processed a significant number of such handwriting SMS, and used various strategies to tackle this challenging area of handwriting recognition. We proposed to study more specifically three different phenomena: consonant skeleton, rebus, and phonetic writing. For each of them, we compare the rough results produced by a standard recognition system with those obtained when using a specific language model.
Emmanuel Prochasson, Christian Viard-Gaudin, Emmanuel Morin
ICDAR1