The Lexical Analysis of Input Text Based on the Term Recognition Algorithm

As the pace of globalization accelerates, translation projects play an increasingly important role in bridging cross-cultural communication. DeepL, a German company that aims to break down language barriers, has injected strong momentum into translation projects with its excellent machine translation services. However, in the refined operation of translation projects, terminology management is still the key to ensuring the accuracy and consistency of translation style. In view of this, this paper explores in depth how to efficiently implement terminology management strategies in DeepL translation projects in order to improve translation quality and operational efficiency. This paper begins with an overview of the origins of DeepL Translator and its built-in terminology management capabilities. Based on the terminology recognition algorithm, the input text is analyzed lexically, the text is split into words or word blocks, and which word blocks may be professional terms are determined. Then a terminology management model for the translation project is built. Through experimental verification, this paper evaluates the actual effectiveness of DeepL’s terminology management in translation practice, which not only highlights its significant advantages but also points out the room for improvement in the translation of professional terminology. The experimental data strongly proves that DeepL’s terminology management function has a high consistency in enhancing translation projects, about 0.85–0.93, but it still needs to be improved in the accurate translation of terminology in specific professional fields.