Fouille de textes

Résumé

La fouille de textes ou « l'extraction de connaissances » dans les textes est une spécialisation de la fouille de données et fait partie du domaine de l'intelligence artificielle. Cette technique est souvent désignée sous l'anglicisme text mining. Elle désigne un ensemble de traitements informatiques consistant à extraire des connaissances selon un critère de nouveauté ou de similarité dans des textes produits par des humains pour des humains. Dans la pratique, cela revient à mettre en algorithme un modèle simplifié des théories linguistiques dans des systèmes informatiques d'apprentissage et de statistiques, et des technologies de compréhension du langage naturel. Les disciplines impliquées sont donc la linguistique calculatoire, l'ingénierie des langues, l'apprentissage artificiel, les statistiques et l'informatique. Mise en œuvre On peut distinguer deux étapes principales dans les traitements mis en place par la fouille de textes. La première étape, l'analyse, cons

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This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students to be able to build such systems as well as read and understand recent research publications.

The objective of this course is to present the main models, formalisms and algorithms necessary for the development of applications in the field of natural language information processing. The concepts introduced during the lectures will be applied during practical sessions.

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)

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Les techniques de data-mining et leur application aux données économiques et industrielles

Ignasi Julia Cano

2002

Chargement

Automatic target validation based on neuroscientific literature mining for tractography

Jocelyne Bloch, Jean-Cédric Chappelier, Bogdan Draganski, Sean Lewis Hill, Renaud Luc Richardet, David Slater, Xavier Vasques

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Frontiers Research Foundation2015

Chargement

Evolution of Topics and Novelty in Science

Orion B Penner

Methods of estimating the similarity between individual publications is an area of long-standing interest in the scientometrics community. Traditional methods have generally relied on references and other metadata, while text mining approaches based on title and abstract text have appeared more frequently in recent years. In principle, Topic Models have great potential in this domain. But in practice, they are often difficult to successfully employ and, in particular, are notoriously inconsistent as latent space dimension grows. That is, running the same model, with the same parameters, on the same data, but with a different random seed produces radically different similarity estimates as the number of topics increase. In this manuscript we develop a simple, but novel, methodology for evaluating the robustness of topic models. Employing that methodology, we find that the neural network based Doc2Vec approach seems capable of providing (statistically) robust estimates of document-document similarities, even for topic spaces far larger than prudent for the most common topic model approach: Latent Dirichlet Allocation. As this is a work in progress, we do not venture deeply into the question of whether these estimates also reflect reality, but do provide some preliminary evidence and future directions for those efforts.

INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI2019

Chargement

Automatic target validation based on neuroscientific literature mining for tractography

Jocelyne Bloch, Jean-Cédric Chappelier, Bogdan Draganski, Sean Lewis Hill, Renaud Luc Richardet, David Slater, Xavier Vasques

Frontiers Research Foundation2015