Natural Language Processing for Information Extraction

TL;DR

This paper reviews the field of Information Extraction in NLP, discussing its sub-tasks, recent advancements, challenges, and future directions to improve handling unstructured textual data.

Contribution

It provides a comprehensive overview of IE sub-tasks, summarizes recent research progress, and outlines key challenges and future research directions in the field.

Findings

Summarizes state-of-the-art IE techniques

Identifies current challenges in IE systems

Suggests future research directions

Abstract

With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and labor intensive. This explosion of information and need for more sophisticated and efficient information handling tools gives rise to Information Extraction(IE) and Information Retrieval(IR) technology. Information Extraction systems takes natural language text as input and produces structured information specified by certain criteria, that is relevant to a particular application. Various sub-tasks of IE such as Named Entity Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction, Knowledge Base reasoning forms the building blocks of various high end Natural Language Processing (NLP) tasks such as Machine Translation, Question-Answering System, Natural Language Understanding, Text Summarization and Digital Assistants like Siri, Cortana and Google Now. This paper introduces Information Extraction technology, its various sub-tasks, highlights state-of-the-art research in various IE subtasks, current challenges and future research directions.