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Found 10 papers, 1 papers with code
NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness
Semantic textual relatedness is a broader concept of semantic similarity.
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.
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.
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.
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.
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT
This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER).
Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service
Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models.
Joint Entity Extraction and Assertion Detection for Clinical Text
Most of the existing systems treat this task as a pipeline of two separate tasks, i. e., named entity recognition (NER) and rule-based negation detection.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data.
