Papers by Abinew Ayele
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages (2024.findings-acl)
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Nedjma Ousidhoum, Shamsuddeen Muhammad, Mohamed Abdalla, Idris Abdulmumin, Ibrahim Ahmad, Sanchit Ahuja, Alham Aji, Vladimir Araujo, Abinew Ayele, Pavan Baswani, Meriem Beloucif, Chris Biemann, Sofia Bourhim, Christine Kock, Genet Dekebo, Oumaima Hourrane, Gopichand Kanumolu, Lokesh Madasu, Samuel Rutunda, Manish Shrivastava, Thamar Solorio, Nirmal Surange, Hailegnaw Tilaye, Krishnapriya Vishnubhotla, Genta Winata, Seid Yimam, Saif Mohammad
| Challenge: | SemRel datasets are annotated by native speakers across 13 languages . they are used to characterise the relationship between two units of text . |
| Approach: | They propose to use a semantic relatedness dataset to measure the degree of semantic textual relatedness between sentences in Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. |
| Outcome: | The proposed datasets are annotated by native speakers across 13 languages and represent the semantic relatedness of 13 languages. |
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages (2023.emnlp-main)
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Shamsuddeen Muhammad, Idris Abdulmumin, Abinew Ayele, Nedjma Ousidhoum, David Adelani, Seid Yimam, Ibrahim Ahmad, Meriem Beloucif, Saif Mohammad, Sebastian Ruder, Oumaima Hourrane, Alipio Jorge, Pavel Brazdil, Felermino Ali, Davis David, Salomey Osei, Bello Shehu-Bello, Falalu Lawan, Tajuddeen Gwadabe, Samuel Rutunda, Tadesse Belay, Wendimu Messelle, Hailu Balcha, Sisay Chala, Hagos Gebremichael, Bernard Opoku, Stephen Arthur
| Challenge: | Africa has the highest linguistic diversity among all continents. |
| Approach: | They introduce a sentiment analysis benchmark that contains >110,000 tweets in 14 African languages . they describe the data collection methodology, annotation process, and challenges . |
| Outcome: | The proposed dataset contains >110,000 tweets in 14 African languages . the tweets were annotated by native speakers and used in the shared task . |
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models (2020.coling-main)
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| Challenge: | Existing crowdsourcing platforms do not support sentiment analysis for Amharic, and there are no expert researchers in the area. |
| Approach: | They propose to build a social-network-friendly Amharic sentiment analysis tool using the Telegram bot and collect 9.4k tweets where each tweet is annotated by three Telegram users. |
| Outcome: | The proposed system outperforms existing classifiers in Amharic and other low-resource languages due to the widespread use of sarcasm and figurative speech. |