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9 results on '"Catherine Berrut"'

1. Combining Bilingual Lexicons Extracted from Comparable Corpora: the Complementary Approach between Word Embedding and Text Mining

Author

Sourour Belhaj Rhouma, Chiraz Latiri, Catherine Berrut, Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Word embedding, business.industry, Computer science, Bilingual dictionary, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Context (language use), 0102 computer and information sciences, 02 engineering and technology, computer.software_genre, ComputingMethodologies_ARTIFICIALINTELLIGENCE, 01 natural sciences, Focus (linguistics), ComputingMethodologies_PATTERNRECOGNITION, Text mining, 010201 computation theory & mathematics, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing, ComputingMilieux_MISCELLANEOUS
Abstract

Recently, different works on bilingual lexicon extraction from comparable corpora have been proposed. This paper presents how to combine differents methods for bilingual lexicon extraction based on standard context vectors and advanced text mining methods. In this respect, we focus on combining bilingual lexicons based on context vectors, association rules and contextual meta-rules. The combination of lexicons leads to a less sparse representation in order to extract the most effective translations from these lexicons and create an optimal bilingual lexicon. An experimental validation conducted on two pairs of languages of the CLEF 2003 campaign evaluation, shows that the combination of the models give a significant improvement compared to the standard approach.

Published
2018

2. A Comparison of Deep Learning Based Query Expansion with Pseudo-Relevance Feedback and Mutual Information

Author

Jean-Pierre Chevallet, Catherine Berrut, Mohannad Almasri, Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM ), Laboratoire d'Informatique de Grenoble (LIG ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Information retrieval, Computer science, business.industry, Deep learning, Relevance feedback, 02 engineering and technology, Mutual information, Machine learning, computer.software_genre, Clef, Term (time), Variety (cybernetics), Query expansion, 020204 information systems, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Language model, business, computer
Abstract

International audience; Automatic query expansion techniques are widely applied for improving text retrieval performance, using a variety of approaches that exploit several data sources for finding expansion terms. Selecting expansion terms is challenging and requires a framework capable of extracting term relationships. Recently, several Natural Language Processing methods , based on Deep Learning, are proposed for learning high quality vector representations of terms from large amounts of unstructured text data with billions of words. These high quality vector representations capture a large number of term relationships. In this paper, we experimentally compare several expansion methods with expansion using these term vector representations. We use language models for information retrieval to evaluate expansion methods. The experiments are conducted on four CLEF collections show a statistically significant improvement over the language models and other expansion models.

Published
2016
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3. Statistical and Semantic Approaches for Tweet Contextualization

Author

Latiri Chiraz, Yahya Slimani, Meriem Amina Zingla, Catherine Berrut, Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), PHC Utique RIMS-FD, National University of Singapore Institute of System SCience (NUS-ISS), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Query expansion, Association rule learning, Computer science, Microblogging, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Twitter, Context (language use), 02 engineering and technology, computer.software_genre, Association rules, Tweet contextualization track, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Social media, Informativeness evaluation, General Environmental Science, Contextualization, business.industry, Spelling, Formal grammar, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], General Earth and Planetary Sciences, Vocabulary mismatch, 020201 artificial intelligence & image processing, Artificial intelligence, InformationSystems_MISCELLANEOUS, business, computer, Natural language processing, Wikipedia
Abstract

International audience; Microblogging sites, like Twitter, have emerged as a popular platform for expressing opinions. Bound to 140 characters, Twitter's publications (tweets) are very short and not always written maintaining formal grammar and proper spelling. The spelling variations increase the likelihood of vocabulary mismatch and make the tweets dicult to understand without some kind of context. The task of tweet contextualization was organized around these issues. It aims to provide an automatic readable context explaining a given tweet, in order to help the reader understand this latter. In this paper, we describe statistical and semantic approaches for the tweet contextualization task. While the statistical one is based on association rules mining, the semantic one uses Wikipedia as an external knowledge source. The e↵ectiveness of our approaches is proved through an experimental study conducted on the INEX 2013 collection.

Published
2015

4. Integrating semantic term relations into information retrieval systems based on language models

Author

Kian Lam Tan, Catherine Berrut, Philippe Mulhem, Mohannad Almasri, Jean-Pierre Chevallet, Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM), Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
Cognitive models of information retrieval, Information retrieval, Computer science, business.industry, Search engine indexing, Relevance feedback, computer.software_genre, Universal Networking Language, Explicit semantic analysis, Semantic computing, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Question answering, Language model, Artificial intelligence, business, computer, Natural language processing, ComputingMilieux_MISCELLANEOUS
Abstract

Most information retrieval systems rely on the strict equality of terms between document and query in order to retrieve relevant documents to a given query. The term mismatch problem appears when users and documents’ authors use different terms to express the same meaning. Statistical translation models are proposed as an effective way to adapt language models in order to mitigate term mismatch problem by exploiting semantic relations between terms. However, translation probability estimation is shown as a crucial and a hard practice within statistical translation models. Therefore, we present an alternative approach to statistical translation models that formally incorporates semantic relations between indexing terms into language models. Experiments on different CLEF corpora from the medical domain show a statistically significant improvement over the ordinary language models, and mostly better than translation models in retrieval performance. The improvement is related to the rate of general terms and their distribution inside the queries.

Published
2014

5. Wikipedia-based Semantic Query Enrichment

Author

Jean-Pierre Chevallet, Mohannad Almasri, Catherine Berrut, Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Semantic query, Information retrieval, Web search query, business.industry, Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, 02 engineering and technology, Query optimization, Query language, computer.software_genre, Semantics, Spatial query, Query expansion, Web query classification, 020204 information systems, query expansion, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, information retrieval, semantic query enrichment, business, computer, Natural language processing
Abstract

International audience; We deal, in this paper, with the short queries (containing one or two words) problem. Short queries have sufficient information to express their semantics in a non-ambiguous way. Pseudo-relevance feedback (PRF) approach for query expansion is useful in many Information Retrieval (IR) tasks. However, this approach does not work well in the case of very short queries. Therefore, we present instead of PRF a semantic query enrichment method based on Wikipedia. This method expands short queries by semantically related terms extracted from Wikipedia. Our experiments on cultural heritage corpora show significant improvement in the retrieval performance.

Published
2013

6. Incomplete and Fuzzy Conceptual Graphs to Automatically Index Medical Reports

Author

Catherine Berrut, Loïc Maisonnasse, Jean-Pierre Chevallet, Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
Information retrieval, business.industry, Computer science, Search engine indexing, Chest ct, 02 engineering and technology, computer.software_genre, Fuzzy logic, Noun phrase, 3. Good health, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020204 information systems, Conceptual graph, 0202 electrical engineering, electronic engineering, information engineering, Conceptual structure, Fuzzy graph, Graph (abstract data type), 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing
Abstract

International audience; Most of Information Retrieval (IR) systems are still based on bag of word paradigm. This is a strong limitation if one needs high precision answers. For example, in restricted domain, like medicine, user builds short and precise query, like âï��ï��Show me chest CT images with emphysema.âï��ï��, and expects from the system precise answers. In such a case, the use of natural language processing to model document content is the only way to improve IR precision. This paper presents a model for text IR that index documents with Fuzzy Conceptual Graphs (FCG). Building automatically a complete and relevant conceptual structure is known to be a difficult task. To overcome this problem and keeping automatic graph building, we promote the use of incomplete FCG. We show how to deal with this incompleteness by using confidence. This confidence is attached to concepts and conceptual relations. As we use FCG as index, the matching process is based on a fuzzy graph matching. Finally, our experiments show that this outperforms classical word based indexing.

Published
2007
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7. Improving New User Recommendations with Rule-based Induction on Cold User Data

Author

Catherine Berrut, An-Te Nguyen, Nathalie Denos, Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Communication Langagière et Interaction Personne-Système (CLIPS - IMAG), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF), Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Information retrieval, Cold start Problem, Computer science, User modeling, media_common.quotation_subject, Recommender Systems, Cold User Data, Computer user satisfaction, Rule-based system, 02 engineering and technology, Recommender system, User requirements document, computer.software_genre, Cold start, Collaborative Filtering, 020204 information systems, New-user Problem. Rule-based Induction, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Collaborative filtering, 020201 artificial intelligence & image processing, Quality (business), Data mining, computer, media_common
Abstract

International audience; With recommender systems, users receive items recommended on the basis of their profile. New users experience the cold start problem: as their profile is very poor, the system performs very poorly. In this paper, classical new user cold start techniques are improved by exploiting the cold user data, i.e. the user data that is readily available (e.g. age, occupation, location, etc.), in order to automatically associate the new user with a better first profile. Relying on the existing alpha-community spaces model, a rule-based induction process is used and a recommendation process based on the "level of agreement" principle is defined. The experiments show that the quality of recommendations compares to that obtained after a classical new user technique, while the new user effort is smaller as no initial ratings are asked.

Published
2007

8. Implementation and analysis of several keyframe-based browsing interfaces to digital video

Author

Noel Murphy, Noel E. O'Connor, Catherine Berrut, Hyowon Lee, Seán Marlow, and Alan F. Smeaton

Subjects
Multimedia, business.industry, Computer science, Interface (computing), Search engine indexing, Digital video, Space (commercial competition), computer.software_genre, Visualization, Digital image, Digital television, business, computer, Multimedia systems, Abstraction (linguistics)
Abstract

In this paper we present a variety of browsing interfaces for digital video information. The six interfaces are implemented on top of Fischlar, an operational recording, indexing, browsing and playback system for broadcast TV programmes. In developing the six browsing interfaces, we have been informed by the various dimensions which can be used to distinguish one interface from another. For this we include layeredness (the number of "layers" of abstraction which can be used in browsing a programme), the provision or omission of temporal information (varying from full timestamp information to nothing at all on time) and visualisation of spatial vs. temporal aspects of the video. After introducing and defining these dimensions we then locate some common browsing interfaces from the literature in this 3-dimensional "space" and then we locate our own six interfaces in this same space. We then present an outline of the interfaces and include some user feedback.

Published
2000

9. Solving grammatical ambiguities within a surface syntactical parser for automatic indexing

Author

Catherine Berrut and Palmer Palmer

Subjects
Surface (mathematics), Parsing, Computer science, business.industry, Process (engineering), Matrix (music), Search engine indexing, computer.software_genre, Syntax, Noun phrase, Automatic indexing, Artificial intelligence, business, computer, Natural language, Natural language processing
Abstract

This paper describes linguistic tools specifically designed for performant automatic indexing of natural language texts. By performant indexing, we mean the ability of the system to extract noun phrases (considered as main conceptual frames regarding text content) without processing full syntactic analysis of sentences (surface analyzer) both with its ability in learning unknown words. The paper describes the overall principles of this parser, emphasizing the use of syntactic networks and precedence matrix to fulfil the above goals of reducing the analysis cost and infering new vocabularies without interrupting the indexing process.

Published
1986
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