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1
Toward an Integrative Approach for Making Sense Distinctions
In: Front Artif Intell (2022)
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2
Cross-lingual Sentence Embedding using Multi-Task Learning ...
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3
Cross-lingual Sentence Embedding using Multi-Task Learning ...
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4
Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages ...
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5
Cross-lingual Sentence Embedding using Multi-Task Learning ...
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6
Unsupervised Deep Language and Dialect Identification for Short Texts ...
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7
Unsupervised Deep Language and Dialect Identification for Short Texts ...
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8
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment
Krek, Simon; Lonke, Dorielle; Ahmad, Raya Abu. - : European Language Resources Association (ELRA), 2020
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9
Automatic morphological analysis and interlinking of historical Irish cognate verb forms
Fransen, Theodorus. - : De Gruyter Mouton, 2020
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10
A comparative study of different state-of-the-art hate speech detection methods in Hindi-English code-mixed data
Fransen, Theodorus; McCrae, John P.; Chakravarthi, Bharathi Raja. - : European Language Resources Association (ELRA), 2020
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11
A multilingual evaluation dataset for monolingual word sense alignment
Nimb, Sanni; Gabrovsek, Dejan; Schoonheim, Tanneke. - : National University of Ireland Galway, 2020
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12
Cardamom: Comparative deep models for minority and historical languages
McCrae, John Philip; Fransen, Theodorus. - : Language Technologies for All (LT4All), 2020
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13
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text ...
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14
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text ...
Abstract: Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed usage in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags instead inferring this automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set. Our results can be found under the user name koustava on the Sentimix Hindi English https://competitions.codalab.org/competitions/20654#learn_the_details-results page. ...
URL: https://zenodo.org/record/4320704
https://dx.doi.org/10.5281/zenodo.4320704
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15
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text ...
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16
A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data ...
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17
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment ...
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18
A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data ...
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19
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment ...
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20
Past, present and future: Computational approaches to mapping historical Irish cognate verb forms
FRANSEN, THEODORUS LEMAN. - : Trinity College Dublin. School of Linguistic Speech & Comm Sci. C.L.C.S., 2019
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