Automatically recognized terminology is widely used for various
domain-specific texts processing tasks, such as machine translation, information
retrieval or ontology construction. However, there is still no agreement on which
methods are best suited for particular settings and, moreover, there is no reliable
comparison of already developed methods. We believe that one of the main reasons
is the lack of state-of-the-art method implementations, which are usually non-trivial
to recreate—mostly, in terms of software engineering efforts. In order to address
these issues, we present ATR4S, an open-source software written in Scala that
comprises 13 state-of-the-art methods for automatic terminology recognition (ATR)
and implements the whole pipeline from text document preprocessing, to term
candidates collection, term candidate scoring, and finally, term candidate ranking. It
is highly scalable, modular and configurable tool with support of automatic caching.
We also compare 13 state-of-the-art methods on 7 open datasets by average precision
and processing time. Experimental comparison reveals that no single method
demonstrates best average precision for all datasets and that other available tools for
ATR do not contain the best methods.

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• ATR4S: toolkit with state-of-the-art automatic terms recognition methods in Scala