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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 55 1 Deceptive Opinions Detection Using New Proposed Arabic Semantic Features Ziani, Amel; Azizi, Nabiha; Schwab, Didier... In: ISSN: 1877-0509 ; EISSN: 1877-0509 ; Procedia Computer Science ; https://hal.archives-ouvertes.fr/hal-03299022 ; Procedia Computer Science, Elsevier, 2021, 189, pp.29 - 36. ⟨10.1016/j.procs.2021.05.067⟩ (2021) BASE Show details 2 Multilingual Epidemic Event Extraction Mutuvi, Stephen; Boros, Emanuela; Doucet, Antoine... In: Towards Open and Trustworthy Digital Societies. 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Virtual Event, December 1–3, 2021, Proceedings ; https://hal.archives-ouvertes.fr/hal-03480551 ; Hao-Ren Ke; Chei Sian Lee; Kazunari Sugiyama. Towards Open and Trustworthy Digital Societies. 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Virtual Event, December 1–3, 2021, Proceedings, 13133, Springer, pp.139-156, 2021, Lecture Notes in Computer Science, 978-3-030-91668-8. ⟨10.1007/978-3-030-91669-5_12⟩ (2021) BASE Show details 3 Multilingual Epidemic Event Extraction ... Mutuvi, Stephen; Boros, Emanuela; Doucet, Antoine. - : Zenodo, 2021 BASE Show details 4 Multilingual Epidemic Event Extraction ... Mutuvi, Stephen; Boros, Emanuela; Doucet, Antoine. - : Zenodo, 2021 BASE Show details 5 Improving Aphasic Speech Recognition by Using Novel Semi-Supervised Learning Methods on AphasiaBank for English and Spanish Iván G. Torre; Mónica Romero; Aitor Álvarez In: Applied Sciences ; Volume 11 ; Issue 19 (2021) BASE Show details 6 BioSGAN: Protein-phenotype Co-mention classification using semi-supervised generative adversarial networks Anokye, Francis; Kahanda, Indika In: UNF Faculty Publications (2021) BASE Show details 7 On semi-supervised LF-MMI training of acoustic models with limited data Sheikh, Imran; Vincent, Emmanuel; Illina, Irina In: INTERSPEECH 2020 ; https://hal.inria.fr/hal-02907924 ; INTERSPEECH 2020, Oct 2020, Shanghai, China (2020) BASE Show details 8 Using External Knowledge to Improve Brown Clustering Miljanic, Veljko. - 2020 BASE Show details 9 Sequence Covering for Efficient Host-Based Intrusion Detection Marteau, Pierre-François In: ISSN: 1556-6013 ; IEEE Transactions on Information Forensics and Security ; https://hal.archives-ouvertes.fr/hal-01653650 ; IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, 2019, 14 (4), pp.994-1006. ⟨10.1109/TIFS.2018.2868614⟩ ; https://ieeexplore.ieee.org/document/8454473 (2019) BASE Show details 10 LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification Jawad Khan; Young-Koo Lee In: Applied Sciences ; Volume 9 ; Issue 24 (2019) Abstract: Sentiment Analysis (SA) is an active research area. SA aims to classify the online unstructured user-generated contents (UUGC) into positive and negative classes. A reliable training data is vital to learn a sentiment classifier for textual sentiment classification, but due to domain heterogeneity, manually construction of reliable labeled sentiment corpora is a laborious and time-consuming task. In the absence of enough labeled data, the alternative usage of sentiment lexicons and semi-supervised learning approaches for sentiment classification have substantially attracted the attention of the research community. However, state-of-the-art techniques for semi-supervised sentiment classification present research challenges expressed in questions like the following. How to effectively utilize the concealed significant information in the unstructured data? How to learn the model while considering the most effective sentiment features? How to remove the noise and redundant features? How to refine the initial training data for initial model learning as the random selection may lead to performance degradation? Besides, mainly existing lexicons have trouble with word coverage, which may ignore key domain-specific sentiment words. Further research is required to improve the sentiment lexicons for textual sentiment classification. In order to address such research issues, in this paper, we propose a novel unified sentiment analysis framework for textual sentiment classification called LeSSA. Our main contributions are threefold. (a) lexicon construction, generating quality and wide coverage sentiment lexicon. (b) training classification models based on a high-quality training dataset generated by using k-mean clustering, active learning, self-learning, and co-training algorithms. (c) classification fusion, whereby the predictions from numerous learners are confluences to determine final sentiment polarity based on majority voting, and (d) practicality, that is, we validate our claim while applying our model on benchmark datasets. The empirical evaluation of multiple domain benchmark datasets demonstrates that the proposed framework outperforms existing semi-supervised learning techniques in terms of classification accuracy. Keyword: active learning; classification fusion; co-training; initial training set; self-training; semi-supervised learning; sentiment lexicons; textual sentiment classification URL: https://doi.org/10.3390/app9245562 BASE Hide details 11 Data quality in the deep learning era: Active semi-supervised learning and text normalization for natural language understanding Lourentzou, Ismini. - 2019 BASE Show details 12 Mineração de opiniões baseada em aspectos para revisões de produtos e serviços ; Aspect-based Opinion Mining for Reviews of Products and Services Yugoshi, Ivone Penque Matsuno. - : Biblioteca Digital de Teses e Dissertações da USP, 2018. : Universidade de São Paulo, 2018. : Instituto de Ciências Matemáticas e de Computação, 2018 BASE Show details 13 Expansão de recursos para análise de sentimentos usando aprendizado semi-supervisionado ; Extending sentiment analysis resources using semi-supervised learning Brum, Henrico Bertini. - : Biblioteca Digital de Teses e Dissertações da USP, 2018. : Universidade de São Paulo, 2018. : Instituto de Ciências Matemáticas e de Computação, 2018 BASE Show details 14 Semi-supervised learning for big social data analysis Hussain, Amir; Cambria, Erik. - : Elsevier, 2018 BASE Show details 15 Strategies to select examples for Active Learning with Conditional Random Fields Claveau, Vincent; Kijak, Ewa In: CICLing 2017 - 18th International Conference on Computational Linguistics and Intelligent Text Processing ; https://hal.archives-ouvertes.fr/hal-01621338 ; CICLing 2017 - 18th International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2017, Budapest, Hungary. pp.1-14 (2017) BASE Show details 16 Semi-supervised learning for big social data analysis Hussain, Amir; Cambria, Erik. - : Elsevier, 2017 BASE Show details 17 Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm Resch, Bernd; Summa, Anja; Zeile, Peter... In: Urban Planning ; 1 ; 2 ; 114-127 (2017) BASE Show details 18 Stratégies de sélection des exemples pour l’apprentissage actif avec des champs aléatoires conditionnels Claveau, Vincent; Kijak, Ewa In: Actes de la conférence TALN 2015 ; Conférence TALN 2015 ; https://hal.archives-ouvertes.fr/hal-01206847 ; Conférence TALN 2015, Jun 2015, Caen, France (2015) BASE Show details 19 Inferring Aspect-Specific Opinion Structure in Product Reviews ... Carter, David. - : Université d'Ottawa / University of Ottawa, 2015 BASE Show details 20 A nonparametric Bayesian perspective for machine learning in partially-observed settings ... Akova, Ferit. - : IUPUI University Library, 2014 BASE Show details Page: 1 2 3

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