Section: New Results

Linking, navigation and analytics

Sentiment analysis on social networks

Participants : Vincent Claveau, Christian Raymond, Vedran Vukotić.

In the framework of our participation to the DeFT 2015 text-mining challenge, we have developped sentiment-analysis methods for tweets [34] . Several sub-tasks have been considered: i) valence classification of tweets and ii) fine-grained classification of tweets (which includes two sub-tasks: detection of the generic class of the information expressed in a tweet and detection of the specific class of the opinion/sentiment/emotion. For all three problems, we adopt a standard machine learning framework. More precisely, three main methods are proposed and their feasibility for the tasks is analyzed: i) decision trees with boosting (bonzaiboost), ii) naive Bayes with Okapi and iii) convolutional neural networks (CNNs). Our approaches are voluntarily knowledge free and text-based only, we do not exploit external resources (lexicons, corpora) or tweet metadata. It allows us to evaluate the interest of each method and of traditional bag-of-words representations vs. word embeddings. Methods using simple ML frameworks and IR-based similarity metrics have been demonstrated to yield the best results.

A multi-dimensional data model for personal photo browsing

Participant : Laurent Amsaleg.

Work performed in the framework of the CNRS PICS MMAnalytics, and in collaboration with Marcel Worring, Univeristy of Amsterdam (The Netherlands)

Digital photo collections—personal, professional, or social—have been growing ever larger, leaving users overwhelmed. It is therefore increasingly important to provide effective browsing tools for photo collections. Learning from the resounding success of multi-dimensional analysis (MDA) in the business intelligence community for on-line analytical processing (OLAP) applications, we proposed a multi-dimensional model for media browsing, called M$ˆ3$, that combines MDA concepts with concepts from faceted browsing [21] . We present the data model and describe preliminary evaluations, made using server and client prototypes, which indicate that users find the model useful and easy to use.

NLP-driven hyperlink construction in broadcast videos

Participants : Rémi Bois, Guillaume Gravier, Pascale Sébillot, Anca-Roxana Şimon.

In collaboration with Sien Moens (Katholieke Universiteit Leuven, Belgium), Éric Jamet and Martin Ragot (Univ. Rennes 2, France).

In the context of the the CominLabs project "Linking media in acceptable hypergraphs" dedicated to the creation of explicit and meaningful links between multimedia documents or fragments of documents, we have introduced a typology of possible links between contents of a multimedia news corpus [32] . While several typologies have been proposed and used by the community, we argue that they are not adapted to rich and large corpora which can contain texts, videos, or radio stations recordings. We have defined a new typology, as a first step towards automatically creating and categorizing links between documents' fragments in order to create new ways to navigate, explore, and extract knowledge from large collections.

We also investigated video hyperlinking based on speech transcripts, leveraging a hierarchical topical structure to address two essential aspects of hyperlinking, namely, serendipity control and link justification [26] . We proposed and compared different approaches exploiting a hierarchy of topic models as an intermediate representation to compare the transcripts of video segments. These hierarchical representations offer a basis to characterize the hyperlinks, thanks to the knowledge of the topics which contributed to the creation of the links, and to control serendipity by choosing to give more weights to either general or specific topics. Experiments have been performed on BBC videos from the Search and Hyperlinking task at MediaEval. Link precisions similar to those of direct text comparison have been achieved however exhibiting different targets along with a potential control of serendipity.

The Search and Anchoring in Video Archives task at MediaEval addressed two issues: The Search part aims at returning a ranked list of video segments that are relevant to a textual user query; The Anchoring part focuses on identifying video segments that would encourage further exploration within the archive. Capitalizing on the experience acquired in previous participations, we implemented a two step approach for both sub-tasks [27] . The first step, common to both, consists in generating a list of potential anchor segments and response-query segments relying on a hierarchical topical structuring technique. In the second step, for each query, the best 20 segments are selected according to content-based comparisons, while for the anchor detection sub-task, the segments are ranked based on a cohesion measure. The use of a hierarchical topical structure helps to propose segments of variable length at different levels of details with precise jump-in points for them. More, the algorithm deriving the structure relies on the burstiness phenomenon in word occurrences which gives an advantage over the classical bag-of-words model.

Information extraction

Participants : Vincent Claveau, Ewa Kijak.

In collaboration with X. Tannier (LIMSI), A. Vilnat (LIMSI) and B. Arnulphy (ANR).

Identifying events from texts is an information extraction task necessary for many NLP applications. Through the TimeML specifications and TempEval challenges, it has received some attention in the last years; yet, no reference result is available for French. In [12] , we try to fill this gap by proposing several event extraction systems, combining for instance Conditional Random Fields, language modeling and k-nearest-neighbors. These systems are evaluated on French corpora and compared with state-of-the-art methods on English. The very good results obtained on both languages validate our whole approach.