Information Extraction tasks such as Named Entity Recognition and Relation Extraction are often developed using diverse tagsets and annotation guidelines. This presents major …
Multidialectal Arabic POS tagging is challenging due to the morphological richness and high variability among dialects. While POS tagging for MSA has advanced thanks to the …
Relation extraction (RE) is a core task in natural language processing, crucial for semantic understanding, knowledge graph construction, and enhancing downstream applications. …
We introduce , a novel multi-dimensional corpus covering 16 Arabic dialects across 10 domains, resulting in 160 distinct corpora. The corpus comprises about 777k tokens, carefully …
We present WojoodNER-2024, the second Arabic Named Entity Recognition (NER) Shared Task. In WojoodNER-2024, we focus on fine-grained Arabic NER. We provided participants with a new …
The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) …
This paper presents an overview of the Arabic Natural Language Understanding (ArabicNLU 2024) shared task, focusing on two subtasks: Word Sense Disambiguation (WSD) and Location …
We introduce SinaTools, an open-source Python package for Arabic natural language processing and understanding. SinaTools is a unified package allowing people to integrate it into …
Semantic textual relatedness is a broader concept of semantic similarity. It measures the extent to which two chunks of text convey similar meaning or topics, or share related …
Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the corpus (550k tokens) as an extension …
We present WojoodNER-2023, the first Arabic Named Entity Recognition (NER) Shared Task. The primary focus of WojoodNER 2023 is on Arabic NER, offering novel NER datasets (i.e., …
SALMA, the first Arabic sense-annotated corpus, consists of 34K tokens, which are all sense-annotated. The corpus is annotated using two different sense inventories simultaneously …
This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain. Our dataset was arabized and localized from the original English Banking77 …
Traditional NER systems are typically trained to recognize coarse-grained entities, and less attention is given to classifying entities into a hierarchy of fine-grained lower-level …
Offensive language detection has been well studied in many languages, but it is lagging behind in low-resource languages, such as Hebrew. In this paper, we present a new offensive …
Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that …
This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. …
Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for in-formation extraction. Most of the …
Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service …
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how …
Clinical predictive modeling involves two challenging tasks, model development and model deployment. In this paper we demonstrate a software architecture for developing and …
The predictive modeling process is time consuming and requires clinical researchers to handle complex electronic health record (EHR) data in restricted computational environments. …
Clinical predictive modeling involves two challenging tasks, model development and model deployment. In this paper we demonstrate a software architecture for developing and …
One of the important properties of SOM is its topology preservation of the input data. The topographic error is one of the techniques proposed to measure how well the continuity of …
Relational fuzzy c-means (RFCM) is an algorithm for clustering objects represented in a pairwise dissimilarity values in a dissimilarity data matrix D. RFCM is dual to the fuzzy …
In this paper we propose a novel fuzzy relational self-organizing map algorithm (FRSOM) that can be used to map a set of n objects described by pairwise dissimilarity values to a …
Disease prediction has become important in a variety of applications such as health insurance, tailored health communication and public health. Disease prediction is usually …