Automatically recognized terminology is widely used for various
domain-specific texts processing tasks, such as machine translation,
information retrieval or sentiment analysis. 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 methods
implementations, which are usually non-trivial to recreate. In order to address
these issues, we present ATR4S, an open-source software written in Scala that
comprises more than 15 methods for automatic terminology recognition (ATR) and
implements the whole pipeline from text document preprocessing, to term
candidates collection, term candidates scoring, and finally, term candidates
ranking. It is highly scalable, modular and configurable tool with support of
automatic caching. We also compare 10 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.

本研究提出了一种名为 ATR4S 的开源软件，涵盖 15 种自动术语识别（ATR）方法，并实现从文本预处理到术语候选人收集，评分和排名的整个流程，与其他可用的 ATR 工具相比，ATR4S 具有更好的平均精度，并可高度可扩展，模块化和可配置。