Patrice Bellot

Accurate and effective latent concept modeling for ad hoc information retrieval

Une requete est la representation du besoin d’information d’un utilisateur, et est le resultat d’un processus cognitif complexe qui mene souvent a un mauvais choix de mots-cles. Nous proposons une methode non supervisee pour la modelisation de concepts implicites d’une requete, dans le but de recreer la representation conceptuelle du besoin d’information initial. Nous utilisons l’allocation de Dirichlet latente (LDA) pour detecter les concepts implicites de la requete en utilisant des documents pseudo-pertinents. Nous evaluons cette methode en profondeur en utilisant deux collections de test de TREC. Nous trouvons notamment que notre approche permet de modeliser precisement les concepts implicites de la requete, tout en obtenant de bonnes performances dans le cadre d’une recherche de documents.

Report on INEX 2008

INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2008 evaluation campaign, which consisted of a wide range of tracks: Ad hoc, Book, Efficiency, Entity Ranking, Interactive, QA, Link the Wiki, and XML Mining.

Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in Twitter

This paper describes our sentiment analysis systems which have been built for SemEval2015 Task 10 Subtask B and E. For subtask B, a Logistic Regression classifier has been trained after extracting several groups of features including lexical, syntactic, lexiconbased, Z score and semantic features. A weighting schema has been adapted for positive and negative labels in order to take into account the unbalanced distribution of tweets between the positive and negative classes. This system is ranked third over 40 participants, it achieves average F1 64.27 on Twitter data set 2015 just 0.57% less than the first system. We also present our participation in Subtask E in which our system has got the second rank with Kendall metric but the first one with Spearman for ranking twitter terms according to their association with the positive sentiment.

Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis

This paper describes our contribution in Opinion Target Extraction OTE and Sentiment Polarity sub tasks of SemEval 2015 ABSA task. A CRF model with IOB notation has been adopted for OTE with several groups of features including syntactic, lexical, semantic, sentiment lexicon features. Our submission for OTE is ranked fifth over twenty submissions. A Logistic Regression model with a weighting schema of positive and negative labels have been used for sentiment polarity; several groups of features (lexical, syntactic, semantic, lexicon and Z score) are extracted. Our submission for Sentiment Polarity is ranked third over ten submissions on the restaurant data set, third over thirteen on the laptops data set, but the first over eleven on the hotel data set that is out-of-domain set.

Automatic annotation of bibliographical references in digital humanities books, articles and blogs

In this paper, we deal with the problem of extracting and processing useful information from bibliographic references in Digital Humanities (DH) data. A machine learning technique for sequential data analysis, Conditional Random Field is applied to a corpus extracted from OpenEdition site, a web platform for journals and book collections in the humanities and social sciences. We present our ongoing project with this purpose that includes the construction of a proper corpus and a efficient CRF model on this as a preliminary. This project is supported by Google Grant for Digital Humanities. A number of experiments are conducted to find one of the best settings for a CRF model on the corpus, and we verify them both in an automatic and manual way of evaluation.

Overview of INEX 2013

INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2013 evaluation campaign, which consisted of four activities addressing three themes: searching professional and user generated data Social Book Search track; searching structured or semantic data Linked Data track; and focused retrieval Snippet Retrieval and Tweet Contextualization tracks. INEX 2013 was an exciting year for INEX in which we consolidated the collaboration with other activities in CLEF and for the second time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums. This paper gives an overview of all the INEX 2013 tracks, their aims and task, the built test-collections, and gives an initial analysis of the results.

The Web as a Source of Evidence for Filtering Candidate Answers to Natural Language Questions

Identifying and extracting named entities from web pages has been the subject of many researches. In this paper, we propose and evaluate some new unsupervised language modeling approaches to determine the membership level of a candidate answer, a named entity, to a natural language question to a very fine-grained conceptual class of entity. We propose to address this issue by using the Web or DBPedia hierarchy as sources of evidence. Then, this level of membership can be used to improve the ranking of candidate answers in a question-answering task. Lastly, we present the results we obtained by participating in TREC 2010 Entity track.

Overview of the 2009 QA track: towards a common task for QA, focused IR and automatic summarization systems

QA@INEX aims to evaluate a complex question-answering task. In such a task, the set of questions is composed of factoid, precise questions that expect short answers, as well as more complex questions that can be answered by several sentences or by an aggregation of texts from different documents. Question-answering, XML/passage retrieval and automatic summarization are combined in order to get closer to real information needs. This paper presents the groundwork carried out in 2009 to determine the tasks and a novel evaluation methodology that will be used in 2010.

Social Recommendation and External Resources for Book Search

In this paper we describe our participation in the INEX 2011 Book Track and present our contributions. This year a brand new collection of documents issued from Amazon was introduced. It is composed of Amazon entries for real books, and their associated user reviews, ratings and tags.

Topic segmentation using weighted lexical links (WLL)

This paper presents two new approaches of lexical chains for topic segmentation using weighted lexical chains (WLC) or weighted lexical links (WLL) between repeated occurrences of lemmas along the text. The main advantage of using these new approaches is the suppression of the empirical parameter called hiatus in lexical chain processing. An evaluation according to the WindowDiff measure on a large automatically built corpus shows slight improvements in WLL compared to state-of-the-art methods based on lexical chains.