In this project we will be performing one of the most famous task in the field of natural language processing i,e Name Entity Recognition.
Named Entity Recognition (NER) is a basic task of Natural Language Processing (NLP). The purpose is to identify named entities such as person names, place names, and organization names in the corpus. Due to the increasing number of these named entities, it is usually impossible to exhaustively list them in the dictionary, and their constituent methods have some regularities. Therefore, the recognition of these words is usually included in the task of morphological processing (such as Chinese segmentation). Independent processing, called named entity recognition.
Named entity recognition technology is an indispensable part of many natural language processing technologies such as information extraction, information retrieval, machine translation, and question answering systems.
Named entities are the research subjects for named entity recognition. Generally, named entities include 3 categories (entity, time, and number) and 7 categories (person, place, institution, time, date, currency, and percentage). Judging whether a named entity is correctly identified includes two aspects: whether the boundary of the entity is correct; and whether the type of the entity is correctly labeled.
The main types of errors include correct text, which may be of the wrong type; conversely, text boundaries are incorrect, and the main entity words and part-of-speech tokens it contains may be correct.
Here we will be using Anaconda Python 3.6 , Pytorch 1.4 with GPU support CUDA 10 with CuDNN 10.