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Using Pop-Culture to Engage Students in the Classroom
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In: ISSN: 0021-9584 ; Journal of Chemical Education ; https://hal.archives-ouvertes.fr/hal-03125040 ; Journal of Chemical Education, American Chemical Society, Division of Chemical Education, In press, ⟨10.1021/acs.jchemed.0c00233⟩ (2021)
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Discovering Acoustic Units from Speech: A Bayesian Approach ; Découverte d'unités acoustiques dans la parole : une approche Bayésienne
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In: https://hal.archives-ouvertes.fr/tel-03478075 ; Computation and Language [cs.CL]. Brno University of Technology (MAIS), 2021. English (2021)
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The dramatic impact of explicit instruction on learning to read in a new writing system ...
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Discovering structure in speech recordings: Unsupervised learning of word and phoneme like units for automatic speech recognition
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In: Fraunhofer IAIS (2021)
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The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
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In: Interspeech 2020 - Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02962224 ; Interspeech 2020 - Conference of the International Speech Communication Association, Oct 2020, Shangai / Virtual, China (2020)
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Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.
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| 7 |
Promover o desenvolvimento lexical desde os primeiros anos: um percurso didático
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DeepTable: a permutation invariant neural network for table orientation classification
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| 10 |
Sihtkeele kultuuriruumist lähtuva identiteedi kasutamine algtaseme võõrkeeleõppes
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In: Eesti Rakenduslingvistika Ühingu Aastaraamat, Vol 16, Pp 109-124 (2020) (2020)
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| 11 |
The Zero Resource Speech Challenge 2019: TTS without T
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In: Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02274112 ; Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association, Sep 2019, Graz, Austria (2019)
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| 12 |
Neural Methods Towards Concept Discovery from Text via Knowledge Transfer
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In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1572387318988274 (2019)
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| 13 |
Desenvolvimento lexical no 1º ano de escolaridade: um percurso didático
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| 15 |
Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery
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| 16 |
The Effects of Guided-discovery, Self-discovery, and Situational-presentation Techniques on Learning Conditional Sentences in English
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In: Applied Linguistics Research Journal, Vol 3, Iss 3, Pp 51-63 (2019) (2019)
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| 17 |
Grammar teaching in Portugal
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In: Bellaterra: journal of teaching and learning language and literature; Vol. 12, Núm. 2 (2019): Juny/Juliol 2019: Monogràfic; p. 21-40 ; Bellaterra Journal of Teaching & Learning Language & Literature; Vol. 12, Núm. 2 (2019): Juny/Juliol 2019: Monogràfic; p. 21-40 (2019)
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| 18 |
Unsupervised Mining and Summarization of Polarized Contentious Issues from Online Text
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Trabelsi, Amine. - : University of Alberta. Department of Computing Science., 2018
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Abstract:
Degree: Doctor of Philosophy ; Abstract: This thesis seeks to contribute to the ongoing research on opinion mining. The contributions are related to the development of newly conceived models for discovery of the viewpoints, and the reasons supporting them, from various polarized contentious texts found in surveys' responses, debate websites, and editorials. This research proposes a purely unsupervised approach without the need for annotated large data or any type of external guidance. It deals only with raw documents consisting of real and unstructured social media text. In this respect, we first suggest a novel Joint Topic Viewpoint (JTV) Bayesian probabilistic model and a modified clustering algorithm to automatically generate idiosyncratic and informative patterns of associated terms denoting a vocabulary for a specific reason. Terms are clustered according to the hidden topics that they discuss and the embedded viewpoint that they voice. The coherence of the distinct reasons' lexicons is shown to be of a high quality. The performance of JTV in clustering exceeds that of state-of-the-art and baseline methods. This out-performance is reiterated for six datasets associated with three different types of contentious documents. Moreover, we formulate a purely unsupervised Author Interaction Topic Viewpoint model (AITV) at the post and the discourse levels. AITV integrates not just the content of the posts, like JTV, but also the reply information about the authors' interactions. The model assumes heterophily when encoding the nature of the authors’ interactions. Heterophily suggests that the difference in viewpoints breeds interactions. We evaluate the model’s viewpoint identification and clustering accuracies at the author and post levels. Experiments are run on six corpora about four different controversial issues, extracted from two online debate forums. AITV’s results show a higher performance in terms of viewpoint identification at the post-level than the state-of-the-art supervised methods in terms of stance prediction. It also outperforms a recently proposed topic model for viewpoint discovery in social networks and achieves close results to a weakly guided unsupervised method in terms of author-level viewpoint identification. Our results highlight the importance of encoding heterophily for purely unsupervised viewpoint identification in the context of online debates. Finally, we design a generic pipeline framework to effectively produce a contrastive textual summary of the main viewpoints given by each of the opposed sides in the form of a fine-grained digest table. The digest table is a realization of the process of automatic extraction and display of the major distinct reasons put forward in the text, according to their topics or facets of argumentation and to their divergent viewpoints. The modular pipeline framework contains a phrase mining, a Topic Viewpoint, and reasons extraction modules. A Phrase Author Interaction Topic Viewpoint model PhAITV is suggested as pipeline component, extending AITV, which jointly processes phrases of different length, instead of just unigrams, and leverages the interaction of authors in online debates. An extensive evaluation of the final produced table is conducted on text about issues extracted from different forums. The evaluation procedure is based on three measures: the informativeness of the digest table as a summary, the relevance of extracted sentences as reasons and the accuracy of their viewpoint clustering. The results on different issues show that our pipeline improves significantly over two state-of-the-art methods and several baselines when measured in terms of documents' summarization, reasons' retrieval, and viewpoint clustering.
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Keyword:
Bayesian Graphical models; Contention Analysis; Contrastive Summarization; Online Debate Forums; Online Social Media Analysis; Text Mining; Topic Viewpoint Modeling and Discovery; Unsupervised Learning
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URL: https://doi.org/10.7939/R36T0HC1M
https://era.library.ualberta.ca/items/42d4dbfd-3b49-4b55-be42-526526d42314
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| 19 |
Unsupervised Speech Unit Discovery Using K-means and Neural Networks
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In: SLSP 2017: Statistical Language and Speech Processing ; 5th International Conference on Statistical Language and Speech Processing (SLSP 2017) ; https://hal.archives-ouvertes.fr/hal-02559766 ; 5th International Conference on Statistical Language and Speech Processing (SLSP 2017), Oct 2017, Le Mans, France. pp.169-180 (2017)
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| 20 |
Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, Proceedings part II
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In: ISSN: 0302-9743 ; Lecture Notes in Computer Science ; 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017) ; https://hal.archives-ouvertes.fr/hal-03120290 ; Benslimane, Djamal; Damiani, Ernesto; Grosky, William I.; Hameurlain, Abdelkader; Sheth, Amit P.; Wagner, Roland R. 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017), Aug 2017, Lyon, France. Lecture Notes in Computer Science, 10439 (Part II), Springer, 2017, Database and Expert Systems Applications 28th International Conference, DEXA 2017, Lyon, France, 978-3-319-64470-7. ⟨10.1007/978-3-319-64471-4⟩ ; https://link.springer.com/book/10.1007%2F978-3-319-64471-4 (2017)
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