Liste de nos séminaires

A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent perception based (fuzzy) words that are commonly used in natural language. Given the wide use of fuzzy words in natural language this limits the strength of these measures in the areas where they are practically applied.

This talk briefly reviews traditional semantic word and sentence similarity measures and then  describes a new fuzzy measure known as FAST (Fuzzy Algorithm for Similarity Testing). FAST is an ontology based similarity measure that uses concepts of fuzzy logic and computing with words to allow for the accurate representation of fuzzy based words. Through empirical human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the fuzzy words. These relationships allowed for the creation of a new ontology based semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. Initial experiments using FAST are described on two possible future benchmark “fuzzy” datasets. The results show that there was an improved level of correlation between FAST and human test results compared with two traditional sentence similarity measures.

The talk concludes by looking at one potential application area where semantic similarity measures are utilised in a Student Debt Advisor Conversational Agent to remove the need for extensive scripting and maintenance.

ABSTRACT:
Due to the Internet revolution, human conversational data--in written forms--are accumulating at a phenomenal rate, as more and more people engage in email exchanges, blogging, texting and other social media activities. In this talk, we will present automatic methods for analyzing conversational text generated in asynchronous conversations, i.e., where participants communicate with each other at different times (e.g., email, blog, forum). Our focus will be on novel techniques to detect the topics covered in the conversation, and to identify whether an utterance in the conversation is expressing an opinion and what its polarity is.

Giuseppe Carenini, Associate Professor
Department of Computer Science
University of British Columbia
carenini@cs.ubc.ca, http://www.cs.ubc.ca/~carenini

BIO:
Giuseppe is an Associate Professor in Computer Science at the University of British Columbia (BC, Canada). He is also a member of the UBC Institute for Computing, Information, and Cognitive Systems (ICICS) and an Associate member of the UBC Institute for Resources, Environment and Sustainability (IRES). Giuseppe has broad interdisciplinary interests. His work on natural language processing and information visualization to support decision making has been published in over 80 peer-reviewed papers. Dr. Carenini was the area chair for “Sentiment Analysis, Opinion Mining, and Text Classification” of ACL 2009 and the area chair for “Summarization and Generation” of NAACL 2012. He has recently co-edited an ACM-TIST Special Issue on “Intelligent Visual Interfaces for Text Analysis”. In July 2011, he has published a co-authored book on “Methods for Mining and Summarizing Text Conversations”. In his work, Dr. Carenini has also extensively collaborated with industrial partners, including Microsoft and IBM. Giuseppe was awarded a Google Research Award and an IBM CASCON Best Exhibit Award in 2007 and 2010 respectively.

Ce travail se positionne dans le cadre de la manipulation de données spatiotemporelles réelles en tenant compte de leur imperfection et plus particulièrement de leur imprécision. La démarche présentée s'inscrit dans la volonté d'aller vers une meilleure gestion de celles-ci tant pour leur représentation que pour leur analyse et leur visualisation. Dans ce contexte, nos contributions portent tant au niveau conception et construction des systèmes d'information géographique que de l'interrogation et l'exploration (possiblement visuelle) des données. Nos méthodes se basent notamment sur la définition de pictogrammes visuels étendant l'UML, d'indices temporels flous, de graphes, de rangs, de coloriage guidé par les données, etc. La démarche sera illustrée autour d'applications sur des données issues de l'archéologie préventive et sur des cas d'études prospectifs en agronomie et en urbanisme.

As a conventional concept of uncertainty, we are familiar with the 'probability' of a phenomenon initiated in 17 century. Also we often discuss the 'uncertainty' of knowledge. Recently, Fuzzy Theory has brought a hidden uncertainty, 'fuzziness', to light. Reflections on these ideas lead to a fundamental question: What kinds of uncertainty are we aware of? Motivated by this question, this study aims to explore categories and modalities of uncertainty. For instance, we have found that (i) 'form' is a category of uncertainty; (ii) 'inconsistency' is a modality of uncertainty; (iii) the inconsistency of form is one of the major uncertainties. Through the classification of adjectives implying various uncertainties, we elucidate seven uncertainties (or nine if subcategories are counted) and identify three essential ones among them, such as the fuzziness of wording. Finally the structure of uncertainty will be shown. The obtained structure is verified by psychological experiments, while the validity of three essential uncertainties is examined by linguistic analysis.

Many models have been proposed over the years to study human movements in general and
handwriting in particular: models relying on neural networks, dynamics models, psychophysical
models, kinematic models and models exploiting minimization principles. Among the models
that can be used to provide analytical representations of a pen stroke, the Kinematic Theory of
rapid human movements and its family of lognormal models has often served as a guide in the
design of pattern recognition systems relying on the exploitation of the fine neuromotricity, like
on-line handwriting segmentation, signature verification as well as in the design of intelligent
systems involving in a way or another, the global processing of human movements. Among
other things, this lecture aims at elaborating a theoretical background for many handwriting
applications as well as providing some basic knowledge that could be integrated or taking care
of in the development of new automatic pattern recognition systems to be exploited in
biomedical engineering and cognitive neurosciences.

More specifically, we will overview the basic neuromotor properties of single strokes and will
explain how they can be superimposed vectorially to generate complex pen tip trajectories.
Doing so, we will report on various projects conducted by our team and our collaborators. First,
we will present a brief comparative survey of the different lognormal models. Then, from a
practical perspective, we will describe some parameter extraction algorithms suitable for the
reverse engineering of individual strokes as well as of complex handwriting signals. We will show
how the resulting representation could be employed to characterize signers and writers and
how the corresponding feature sets could be exploited to study the effects of various factors,
like aging and health problems, on handwriting variability. We will also describe some
methodologies to generate automatically huge on-line handwriting databases for either writer
dependent or writer independent applications as well as for the production of synthetic
signature databases. From a theoretical perspective, we will explain how, using an original
psychophysical set up, we have been able to validate the basic hypothesis of the Kinematic
Theory and to test its most distinctive predictions. We will complete this survey by explaining
how the Kinematic Theory could be utilized to improve some signal processing techniques,
opening a window on novel potential applications for on-line handwriting processing,
particularly to provide some benchmarks to analyze children handwriting learning, to study
aging effects on neuromotor control as well as developing diagnostic systems for
neuromuscular disorders. To illustrate this latter point, we will report typical results obtained so
far for the assessment of brain stroke most important modifiable risk factors (diabetes,
hypertension, hypercholesterolemia, obesity, cardiac problems, cigarette smoking).

Using simulations, we have first shown that, thanks to the physiological learning mechanism referred to as Spike Timing-Dependent Plasticity (STDP), neurons can detect and learn repeating spike patterns, in an unsupervised manner, even when those patterns are embedded in noise[1,2]. Importantly, the spike patterns do not need to repeat exactly: it also works when only a firing probability pattern repeats, providing this profile has narrow (10-20ms) temporal peaks[3]. Brain oscillations may help in getting the required temporal precision[4], in particular when dealing with slowly changing stimuli. All together, these studies show that some envisaged problems associated to spike timing codes, in particular noise-resistance, the need for a reference time, or the decoding issue, might not be as severe as once thought. These generic STDP-based mechanisms are probably at work in particular the visualsystem, where they can explain how selectivity to visual primitives emerges[5,6], leading to very reactive systems. I am now investigating if they are also at work in the somatosensory system. Finally, these mechanisms are also appealing for neuromorphic engineering: they can be efficiently implemented in hardware, leading to fast systems with self-learning abilities[7].

References
1 Masquelier, T. et al. (2008) Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE 3, e1377
2 Masquelier, T. et al. (2009) Competitive STDP-Based Spike Pattern Learning. Neural Comput 21, 1259–1276
3 Gilson, M. et al. (2011) STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 7, e1002231
4 Masquelier, T. et al. (2009) Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. The Journal of neuroscience 29, 13484–93
5 Masquelier, T. and Thorpe, S.J. (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3, e31
6 Masquelier, T. (2012) Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model. Journal of computational neuroscience 32, 425–41
7 Zamarreño-Ramos, C. et al. (2011) On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex. Frontiers in neuroscience 5, 22

The United Nations Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has developed the International Monitoring System, a global network of seismic stations, to detect potential treaty violations. CTBTO software analyses the signals from this network to detect and localize the seismic events that caused them.  This analysis problem can be reformulated in a Bayesian framework.  I will describe a Bayesian seismic monitoring system, NET-VISA, based on generative probabilistic models of event occurrence and signal transmission and detection. NET-VISA reduces the number of missed events by a factor of 2 to 3 compared to the currently deployed system. It also finds events that are missed even by CTBTO's expert analysts.

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* L'orateur est soutenu par, et cette présentation est donnée sous les auspices de, la Chaire Internationale de Recherche Blaise Pascal financée par l’État et la Région d’Ile de France, gérée par la Fondation de l'École Normale Supérieure.
The speaker is supported by, and this talk is given under the auspices of, the International Research Chaire Blaise Pascal, funded by the French State and Ile de France Region and administered by the Fondation de l'École Normale Supérieure.

En psychologie cognitive, la notion de prototype apparaît de manière centrale dans les représentations conceptuelles. Dans le cadre de nos travaux, nous proposons d'introduire cette notion au sein des activités relevant de l'Ingénierie des Ontologies et de ses modèles de représentation. L'approche sémiotique que nous avons développée est fondée sur les trois dimensions d'une conceptualisation que sont l'intension (les propriétés), l'expression (les termes), et l'extension (les instances). Elle intègre, au sein de l'ontologie, des connaissances supplémentaires propres à l'utilisateur (pondération des propriétés, corpus, instances). Pratiquement, il s'agit de pondérer les liens "is-a", les termes et les instances d'une hiérarchie de concepts, au moyen de gradients de prototypicalité respectivement conceptuelle, lexicale et extensionnelle.  Nous avons étendu notre approche à la définition de deux nouvelles mesures sémantiques, en nous inspirant des lois de similarité et de proximité de la théorie de la perception. Nous présenterons différents use case de ces mesures (recherche d'information, validation d'ontologies, etc.).