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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 Hits 1 – 10 of 10 1 Cross-lingual Sentence Embedding using Multi-Task Learning ... Koustava Goswami; Sourav Dutta; Assem, Haytham. - : Zenodo, 2021 BASE Show details 2 Cross-lingual Sentence Embedding using Multi-Task Learning ... Koustava Goswami; Sourav Dutta; Assem, Haytham. - : Zenodo, 2021 BASE Show details 3 ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021 ... Ojha, Atul Kr.; Rani, Priya; Koustava Goswami. - : Zenodo, 2021 BASE Show details 4 ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021 ... Ojha, Atul Kr.; Rani, Priya; Koustava Goswami. - : Zenodo, 2021 BASE Show details 5 Unsupervised Deep Language and Dialect Identification for Short Texts ... Koustava Goswami; Rajdeep Sarkar; Bharathi Raja Chakravarthi; Fransen, Theodorus; McCrae, John P.. - : Zenodo, 2020 Abstract: Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language identification is considered a solved task in many cases; however, in the case of very closely related languages, or in an unsupervised scenario (where the languages are not known in advance), performance is still poor. In this paper, we propose the Unsupervised Deep Language and Dialect Identification (UDLDI) method, which can simultaneously learn sentence embeddings and cluster assignments from short texts. The UDLDI model understands the sentence constructions of languages by applying attention to character relations which helps to optimize the clustering of languages. We have performed our experiments on three short-text datasets for different language families, each consisting of closely related languages or dialects, with very minimal training sets. Our experimental evaluations on these datasets ... URL: https://zenodo.org/record/4320719 https://dx.doi.org/10.5281/zenodo.4320719 BASE Hide details 6 Unsupervised Deep Language and Dialect Identification for Short Texts ... Koustava Goswami; Rajdeep Sarkar; Bharathi Raja Chakravarthi. - : Zenodo, 2020 BASE Show details 7 ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text ... Koustava Goswami; Rani, Priya; Bharathi Raja Chakravarthi. - : Zenodo, 2020 BASE Show details 8 ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text ... Koustava Goswami; Rani, Priya; Bharathi Raja Chakravarthi. - : Zenodo, 2020 BASE Show details 9 A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data ... Rani, Priya; Shardul Suryawanshi; Koustava Goswami. - : Zenodo, 2020 BASE Show details 10 A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data ... Rani, Priya; Shardul Suryawanshi; Koustava Goswami. - : Zenodo, 2020 BASE Show details

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