# A Scalable Document-based Architecture for Text Analysis

arXiv: https://arxiv.org/abs/1612.06195 · HTML: https://mlsanity.com/paper/1612.06195/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)
- [StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms](https://mlsanity.com/paper/2411.06138/) (2024)
- [GETAE: Graph information Enhanced deep neural NeTwork ensemble ArchitecturE for fake news detection](https://mlsanity.com/paper/2412.01825/) (2024)

## Abstract (arXiv)

Analyzing textual data is a very challenging task because of the huge volume of data generated daily. Fundamental issues in text analysis include the lack of structure in document datasets, the need for various preprocessing steps %(e.g., stem or lemma extraction, part-of-speech tagging, named entities recognition...), and performance and scaling issues. Existing text analysis architectures partly solve these issues, providing restrictive data schemas, addressing only one aspect of text preprocessing and focusing on one single task when dealing with performance optimization. %As a result, no definite solution is currently available. Thus, we propose in this paper a new generic text analysis architecture, where document structure is flexible, many preprocessing techniques are integrated and textual datasets are indexed for efficient access. We implement our conceptual architecture using both a relational and a document-oriented database. Our experiments demonstrate the feasibility of our approach and the superiority of the document-oriented logical and physical implementation.