A Distributed Automatic Domain-Specific Multi-Word Term Recognition
  Architecture using Spark Ecosystem
Ciprian-Octavian Truică Neculai-Ovidiu Istrate Elena-Simona Apostol
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.