Analyse automatique de textes littéraires et scientifiques : présentation et résultats du défi fouille de texte DEFT2014
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In this paper, we present the 2014 DEFT text mining shared task, dedicated to the analysis of literature texts (corpus Short Edition) and scientific texts (TALN archives) through four tasks: identifying the literary type, eval- uating writing quality, determining whether the quality of a work achieves consensus among the reviewers, and finally identifying the conference session of a scientific paper. In order to evaluate the results, we used normalized discounted cumulative gain (NDCG, task 1), accuracy of the relative distance to the mean solution (EDRM, task 2), precision (task 3), and correction (task 4). The results obtained by the participants are highly contrasted and reveal the difficulty of each task, although one system reached the maximal performance in task 4. Mots-cles : Fouille d'opinion, classification automatique, evaluation.Cite
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Abstract The tasks and structure for the first psychotherapy session or intake session are outlined in this chapter. The reader is encouraged to organize themselves and their paperwork prior to the first session and be sure that information about common referrals is at hand. Topics include time management, tasks to accomplish, taking notes, starting the session, identifying the presenting problem, establishing rapport, identifying current life problems, making a diagnosis, and evaluating crisis risk. The importance of taking ample notes during any initial meeting is discussed. Two client case examples illustrate these points throughout the chapter. Ending the session effectively and the psychotherapist’s reaction to the session are discussed.
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Natural language processing (NLP) is an area of machine learning that has garnered a lot of attention in recent days due to the revolution in artificial intelligence, robotics, and smart devices. NLP focuses on training machines to understand and analyze various languages, extract meaningful information from those, translate from one language to another, correct grammar, predict the next word, complete a sentence, or even generate a completely new sentence from an existing corpus. A major challenge in NLP lies in training the model for obtaining high prediction accuracy since training needs a vast dataset. For widely used languages like English, there are many datasets available that can be used for NLP tasks like training a model and summarization but for languages like Bengali, which is only spoken primarily in South Asia, there is a dearth of big datasets which can be used to build a robust machine learning model. Therefore, NLP researchers who mainly work with the Bengali language will find an extensive, robust dataset incredibly useful for their NLP tasks involving the Bengali language. With this pressing issue in mind, this research work has prepared a dataset whose content is curated from social media, blogs, newspapers, wiki pages, and other similar resources. The amount of samples in this dataset is 19132010, and the length varies from 3 to 512 words. This dataset can easily be used to build any unsupervised machine learning model with an aim to performing necessary NLP tasks involving the Bengali language. Also, this research work is releasing two preprocessed version of this dataset that is especially suited for training both core machine learning-based and statistical-based model. As very few attempts have been made in this domain, keeping Bengali language researchers in mind, it is believed that the proposed dataset will significantly contribute to the Bengali machine learning and NLP community.
Sentiment Analysis
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Chapter 7 outlines the tenth session of treatment, and reviews progress that the child has made to date, troubleshoots obstacles that may be causing lack of progress, and begins to involve the child's teacher in out-of-session exposure tasks.
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This chapter contains sections titled: Overview Before the Session The Session - Part 1 The Session - Part 2 Key Cognitions to Elicit and Challenge Don't Forget …
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CONTEST
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Two multi-session experiments are described in which a complex problem-solving task was interrupted at different stages of practice. In Experiment 1, subjects practiced the main problem-solving task for three sessions, with intermittent interruptions during each session. By the end of Session 3, interruptions which were similar to the main task, in terms of type of material processed and processing demands, no longer disrupted performance as they had in Sessions 1 and 2. In Experiment 2, subjects practiced the same problem-solving task for two sessions without interruptions. The same types of interruptions used in Experiment 1 were introduced in Session 3. Although the main task was well learned by the third session, the interruptions disrupted subjects' main-task accuracies dramatically. These results suggest that training tasks under uninterrupted conditions can lead to excellent performance, but may not allow subjects to develop the kinds of strategies needed to flexibly recover from interruptions when they occur.
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