C-NER : Complex Named Entity Recognition

Abstract

Nowadays, a large amount of unstructured data can be obtained from various sources. These include social media, email, news articles, chat data, etc. Through processing this data, there is a possibility to get some useful information. We can identify named entity recognition as a prominent part of natural language processing. Named Entity Recognition is the process in which data is given a proper name or a tag for data needed to make findings like place names, person names or organizations, and they are identified as an entity. It's also known as entity extraction, entity chunking, or entity identification. We may extract vital information to interpret the text using named entity recognition, or we can utilize it to extract vital information to record in a database. The system is essentially a machine learning tool. As a result, it will be able to use a set of raw text data as the system's principal input. Then as the primary process, this system detects words separately one by one. This research project aims to design, develop and evaluate a system that recognizes the complex named entities like the titles of creative work from a given set of words and test the domain capability of the system by trying on extra test sets on questions and short search queries. The complex and syntactic ambiguity entities make it challenging to recognize them based on the content. For this process, it should consider the meaning of the sentence. According to that only, the system can predict the correct entities and output the labelled text document.