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For STM publishers, exceptional language quality is non-negotiable. The crucial language editing process therefore needs to be robust and continually improving, while finding ways to boost efficiency gains. Smart Language assigns a language score and an editing level to manuscripts, so publishers can route them to the most appropriate editing teams/suppliers.
Built on a convolutional neural network, Smart Language leverages deep learning models and linguistically informed rule-based systems. It evaluates content based on sentence structure, parts-of-speech components, text sequences, spellings, and word similarity patterns on a sentence level, aggregating it all to the journal article or book chapter. Smart Language has been 3 years in the making, built from our 25 years of copy editing experience and in partnership with Enagoʼs Trinka, which was used as part of Smart Languageʼs core.
Before Smart Language, Elsevier would assign a copy editing level to all articles in a journal, when in reality the language quality could vary greatly from article to article within every journal. Assessing every article in detail, however, takes significant manual effort.
TNQ’s product team worked with Elsevier to identify areas where we could largely automate language quality assessment, speed up turnaround time, and reduce costs. The ultimate goal was to improve the overall quality of these assessments, while preserving author satisfaction. Since November 2021, across 10,000 articles and 100,000 pages (ramping up to 20,000 articles in 2023), Smart Language has helped:
| Before Smart Language | Since Smart Language |
|---|---|
| Manual assessment of manuscripts for language quality | ML-based language profiler assesses for language quality |
| Copy editing service levels defined for entire journal | Copy editing service levels defined by article |
| Fixed turnaround time and cost | Faster turnaround time and reduced cost |
Smart Language is part of TNQ’s Smart Central – a suite of microservices.
About the author: Shanthi Krishnamoorthy is the Chief Domain Officer, heading R&D at TNQ. Her background in research and her love for books on science and human beings mean she is always encouraging all of us to read more! Shanthi has been with TNQ since the very beginning, lighting up the room when she walks in and leading with warmth and compassion.
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Our expertise in the publishing industry, a rigorous training programme, and a technology-driven production process allow us to maintain the highest level of quality in everything that we deliver. Our quality control framework was created primarily to understand and document customer requirements, as well as to implement data-driven internal and external quality metrics that evaluate people, processes, and technology.
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