LSTM Based Named Entity Chunking and Entity Extraction

Abstract

Some Natural Language Processing (NLP) jobs require the automatic extraction of key information from a text document, which is why automatic extraction is required. With the rise of social media, digital journalism, and blogging, automatic extraction is becoming increasingly important. The amount of information available is enormous, and information extraction will aid in the management of such vast amounts of data. A important subtask of automatic information extraction is named entity recognition (NER), also known as entity identification, entity chunking, and entity extraction. NER is also known as entity chunking and entity extraction. In an unstructured text document, it locates and categorises the identified entities with unique significance by categorising them into pre-defined categories like person, organisation, location, and so on. In a large number of occasions, this contain the most important information about the document. There are numerous applications for this information. It can be used to improve the ordering and filtering of key terms in documents, or it can simply be used as an input to NLP activities such as text summarization, question answering, and machine translation, among other things..