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Found 19 papers, 3 papers with code
SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks
To establish a Word Sense Disambiguation baseline using our SALMA corpus, we developed an end-to-end Word Sense Disambiguation system using Target Sense Verification.
ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic
This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain.
Nabra: Syrian Arabic Dialects with Morphological Annotations
This paper presents Nabra, a corpora of Syrian Arabic dialects with morphological annotations.
Arabic Fine-Grained Entity Recognition
To compute the baselines of WojoodF ine, we fine-tune three pre-trained Arabic BERT encoders in three settings: flat NER, nested NER and nested NER with subtypes and achieved F1 score of 0. 920, 0. 866, and 0. 885, respectively.
WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task
The winning teams achieved F1 scores of 91. 96 and 93. 73 in FlatNER and NestedNER, respectively.
Offensive Hebrew Corpus and Detection using BERT
Fine-tuning AlephBERT on our data and testing on D_OLaH yields 69% accuracy, while fine-tuning on D_OLaH and testing on our data yields 57% accuracy, which may be an indication to the generalizability our data offers.
Context-Gloss Augmentation for Improving Arabic Target Sense Verification
The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consists of 167K context-gloss pairs (about 60K positive and 107K negative pairs), collected from Arabic dictionaries.
A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms
Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate.
Lisan: Yemeni, Iraqi, Libyan, and Sudanese Arabic Dialect Copora with Morphological Annotations
This article presents morphologically-annotated Yemeni, Sudanese, Iraqi, and Libyan Arabic dialects Lisan corpora.
Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic
The third approach is to construct new WordNets by exploring synonymy graphs, and the fourth approach is to find similar words from corpora using Deep Learning methods, such as word embeddings and recently BERT models.
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT
This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER).
The Arabic Ontology -- An Arabic Wordnet with Ontologically Clean Content
The ontology provides a formal representation of the concepts that the Arabic terms convey, and its content was built with ontological analysis in mind, and benchmarked to scientific advances and rigorous knowledge sources as much as this is possible, rather than to only speakers' beliefs as lexicons typically are.
ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD
First, we constructed a dataset of labeled Arabic context-gloss pairs (~167k pairs) we extracted from the Arabic Ontology and the large lexicographic database available at Birzeit University.
Curras + Baladi: Towards a Levantine Corpus
This is due to many factors, including the complex and rich morphology of Arabic, its high degree of ambiguity, and the presence of several regional varieties that need to be processed while taking into account their unique characteristics.
LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation
This paper presents a set of experiments to evaluate and compare between the performance of using CBOW Word2Vec and Lemma2Vec models for Arabic Word-in-Context (WiC) disambiguation without using sense inventories or sense embeddings.
Extracting Synonyms from Bilingual Dictionaries
We present our progress in developing a novel algorithm to extract synonyms from bilingual dictionaries.
A Panoramic Survey of Natural Language Processing in the Arab World
The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design.
