# Natural Language Processing for Information Extraction

arXiv: https://arxiv.org/abs/1807.02383 · HTML: https://mlsanity.com/paper/1807.02383/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 14 of 14)

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- [Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2107.04382/) (2021)
- [Facilitating automated conversion of scientific knowledge into scientific simulation models with the Machine Assisted Generation, Calibration, and Comparison (MAGCC) Framework](https://mlsanity.com/paper/2204.10382/) (2022)
- [Measuring and Mitigating Hallucinations in Vision-Language Dataset Generation for Remote Sensing](https://mlsanity.com/paper/2501.14905/) (2025)
- [A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction](https://mlsanity.com/paper/2402.11177/) (2024)
- [CEHA: A Dataset of Conflict Events in the Horn of Africa](https://mlsanity.com/paper/2412.13511/) (2024)
- [Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish](https://mlsanity.com/paper/2503.18539/) (2025)
- [Automated Generation of Precedence Graphs in Digital Value Chains for Automotive Production](https://mlsanity.com/paper/2504.19835/) (2025)

## Abstract (arXiv)

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.