Dor Getter, Aiman Younis, Liad Ben Moshe
In any text content, there are some terms that are more informative and unique in context. Named Entity Recognition (NER) also known as information extraction/chunking is the process in which algorithm extracts the real world noun entity from the text data and classifies them into predefined categories like person, place, time, organization, etc.
In our project we fine-tuned BERT models to perform named-entity recognition (NER) in three languages (English, Hebrew and Arabic). The project provides web-based interface (written in REACT) that allows the user to enter text in one of the languages and get an output of the text labeled into entities, and a table that shows for each word in the text what is the entity, and the confidence score.
Please refer to our Project Book
Link https://github.com/DorGetter/NER_Project/blob/main/Project%20Book.pdf
PhD Or Anidgar Lecturer, Department of Computer Science at Ariel University for Deep Learning Methods for Natural Language Processing & Speech Recognition
Credits The Hebrew Treebank: http://www.cs.technion.ac.il/~itai/publications/NLP/TreeBank.pdf The SPMRL Shared Task data: https://www.aclweb.org/anthology/W13-4917/ The Universal Dependencies Treebank: https://www.aclweb.org/anthology/W18-6016 The Named Entity Recognition Annotations: https://arxiv.org/abs/2007.15620 The Hebrew Sentiment Analysis Corpus: https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew Tobias Sterbak: https://www.depends-on-the-definition.com/named-entity-recognition-with-bert/