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Found 13 papers, 2 papers with code
ALUE: Arabic Language Understanding Evaluation
The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU).
A Taxonomic Classification of WordNet Polysemy Types
WordNet represents polysemous terms by capturing the different meanings of these terms at the lexical level, but without giving emphasis on the polysemy types such terms belong to.
Advancing the Arabic WordNet: Elevating Content Quality
High-quality WordNets are crucial for achieving high-quality results in NLP applications that rely on such resources.
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text
We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023.
Lexical Diversity in Kinship Across Languages and Dialects
The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages.
Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship
We capture the phenomenon of diversity through the notions of lexical gap and language-specific word and use a systematic method to infer gaps semi-automatically on a large scale.
A Major Wordnet for a Minority Language: Scottish Gaelic
We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60, 000 speakers, most of whom live in Northwestern Scotland.
SemEval-2016 Task 3: Community Question Answering
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic.
ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification
This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task.
Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning
In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1).
TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers
In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al., 2017). We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy. Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis. Our system obtained a comparable resultsto Machine learning systems.
BIG-bench Machine Learning
Named Entity Recognition (NER)
+1
