# A Distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem

arXiv: https://arxiv.org/abs/2305.16343 · HTML: https://mlsanity.com/paper/2305.16343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 3 of 3)

- [TextBenDS: a generic Textual data Benchmark for Distributed Systems](https://mlsanity.com/paper/2108.05689/) (2021)
- [Performance evaluation for CRUD operations in asynchronously replicated document oriented database](https://mlsanity.com/paper/1806.04761/) (2018)
- [The Forgotten Document-Oriented Database Management Systems: An Overview and Benchmark of Native XML DODBMSes in Comparison with JSON DODBMSes](https://mlsanity.com/paper/2102.02246/) (2021)

## Built on by (most cited first, 1 of 1)

- [GETAE: Graph information Enhanced deep neural NeTwork ensemble ArchitecturE for fake news detection](https://mlsanity.com/paper/2412.01825/) (2024)

## Abstract (arXiv)

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem; (2) perform an in-depth analysis of our architecture in terms of accuracy and scalability; (3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets.