Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the
effort of manually identifying terms from domain-specific corpora by providing a list of …

The TermEval 2020 shared task provided a platform for researchers to work on automatic
term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction …

Automatic term extraction is a productive field of research within natural language
processing, but it still faces significant obstacles regarding datasets and evaluation, which …

We propose a machine learning method to automatically classify the extracted ngrams from
a corpus into terms and non-terms. We use 10 common statistics in previous term extraction …

The creation of crowd-sourced content in learning systems is a powerful method for adapting
learning systems to the needs of a range of teachers in a range of domains, but the quality of …

This paper presents the first results of a new method for terminology extraction based on
distributional analysis. The intuition behind the algorithm is that single or multi-word lexical …

Specialised texts are characterised by, amongst other features, the presence of terminology
which conveys domain-specific concepts that are essential for the specialist who is …

One of the most remarkable features of the legal English lexicon is the use of sub-technical
vocabulary, that is, words frequently shared by the general and specialised fields which …

In this paper, we describe a methodology used to create a test corpus for the evaluation of
term extractors. This methodology relies on term annotation: terms in a corpus on automotive …

There are several methods and available tools for terminology extraction, but the quality of
the extracted terms is not always high. Hence, an important consideration in terminology …