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Träfflista för sökning "WFRF:(Yimam Seid Muhie) "

Sökning: WFRF:(Yimam Seid Muhie)

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1.
  • Adelani, David Ifeoluwa, et al. (författare)
  • MasakhaNER: Named Entity Recognition for African Languages
  • 2021
  • Ingår i: Transactions of the Association for Computational Linguistics. - : MIT Press. - 2307-387X. ; 9, s. 1116-1131
  • Tidskriftsartikel (refereegranskat)abstract
    • We take a step towards addressing the under-representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.
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2.
  • Beloucif, Meriem, et al. (författare)
  • Elvis vs. M. Jackson : Who has More Albums? Classification and Identification of Elements in Comparative Questions
  • 2022
  • Ingår i: LREC 2022. - : European Language Resources Association. - 9791095546726 ; , s. 3771-3779
  • Konferensbidrag (refereegranskat)abstract
    • Comparative Question Answering (cQA) is the task of providing concrete and accurate responses to queries such as: "Is Lyft cheaper than a regular taxi?" or "What makes a mortgage different from a regular loan?". In this paper, we propose two new open-domain real-world datasets for identifying and labeling comparative questions. While the first dataset contains instances of English questions labeled as comparative vs. non-comparative, the second dataset provides additional labels including the objects and the aspects of comparison. We conduct several experiments that evaluate the soundness of our datasets. The evaluation of our datasets using various classifiers show promising results that reach close-to-human results on a binary classification task with a neural model using ALBERT embeddings. When approaching the unsupervised sequence labeling task, some headroom remains.
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3.
  • Muhammad, Shamsuddeen Hassan, et al. (författare)
  • SemEval-2023 Task 12 : Sentiment Analysis for African Languages (AfriSenti-SemEval)
  • 2023
  • Ingår i: Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). - : Association for Computational Linguistics. - 9781959429999 ; , s. 2319-2337
  • Konferensbidrag (refereegranskat)abstract
    • We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.
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  • Resultat 1-3 av 3

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