Named Entity Recognition Approaches and Their Comparison for Custom NER Model

ABSTRACT

Named entity recognition (NER) is a natural language processing tool for information extraction from unstructured text data such as e-mails, newspapers, blogs, etc. NER is the process of identifying nouns like people, place, organization, etc., that are mentioned in the string of the text, sentence, or paragraph. For building the NER system, many different libraries and natural language processing tools using Java, Python, and Cython languages are available. All these tools have pretrained NER models that can be imported, used and can be modified or customized according to requirements. This paper explains different NLP libraries including Python’s SpaCy, Apache OpenNLP, and TensorFlow. Some of these libraries provide a pre-build NER model that can be customized. The comparison of these libraries is done based on training accuracy, F-score, prediction time, model size, and ease of training. The training and testing data are the same for all the models. When considering the overall performance of all the models, Python’s Spacy gives a higher accuracy and the best result.

KEYWORDS:

• Named entity recognition (NER)
• custom named entity recognition (CNER)
• natural Language Processing (NLP)
• bio Tagging
• spacy
• OpenNLP
• TensorFlow