Fact Recognition and Extraction Techniques in Natural Language Processing

Abstract

Natural Language Processing has become one of the most useful areas of computer science, especially when it comes to understanding and working with large amounts of text data. One important task within NLP is fact recognition and extraction — which basically means identifying useful facts from unstructured text and organizing them in a way that machines can process. This paper studies the main techniques used for this purpose, including Named Entity Recognition (NER), relation extraction, and Open Information Extraction (OpenIE). The role of modern deep learning models, especially BERT, is also discussed. The study is based on reviewing existing research and published papers in this area. It was found that while deep learning methods have greatly improved the accuracy of fact extraction, there are still some challenges like handling ambiguous language and making these systems work across different domains.

Keywords: Natural Language Processing, Fact Extraction, Named Entity Recognition, Relation Extraction, Deep Learning.