Boosting Arabic Named-Entity Recognition With Multi-Attention Layer

Arabic Named Entity Recognition

Stemming is the process of reducing words to their stems or roots. Due to the morphological richness and complexity of the Arabic language, stemming is an essential part of most Natural Language Processing (NLP) tasks for this language. In this paper, we study the impact of different stemming approaches on the Named Entity Recognition (NER) task for Arabic and explore the merits, limitations an...

Boosting for Named Entity Recognition

Boosting for Chinese Named Entity Recognition

We report an experiment in which a highperformance boosting based NER model originally designed for multiple European languages is instead applied to the Chinese named entity recognition task of the third SIGHAN Chinese language processing bakeoff. Using a simple characterbased model along with a set of features that are easily obtained from the Chinese input strings, the system described emplo...

Fine-grained Arabic named entity recognition

Named Entity Recognition (NER) is a Natural Language Processing (NLP) task, which aims to extract useful information from unstructured textual data by detecting and classifying Named Entity (NE) phrases into predefined semantic classes. This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and sho...

Named Entity Recognition for Arabic Social Media

The majority of research on Arabic Named Entity Recognition (NER) addresses the the task for newswire genre, where the language used is Modern Standard Arabic (MSA), however, the need to study this task in social media is becoming more vital. Social media is characterized by the use of both MSA and Dialectal Arabic (DA), with often code switching between the two language varieties. Despite some...