Natural Language Processing Defined
Natural language processing (NLP) is a branch of linguistics, computer science, and artificial intelligence focusing on the interactions between computers and human languages. Specifically, it is concerned with how to program computers to process and analyze large amounts of language data. The goal of natural language processing is to make human language easy for machines to understand. As a result, natural language processing uses this understanding of complex languages to perform different tasks automatically.
It's likely that you utilize natural language processing on a daily basis without even realizing it. Did you ask your Alexa to turn your lights on this morning? Ask Siri for directions to the nearest coffee shop? Even something as simple as predictive text or websites automatically translating to your preferred language are examples of natural language processing that you are likely using every day.
The current challenges with natural language processing include speech recognition, understanding languages, and natural language generation, though this is a continuous work in progress. In fact, a new artificial intelligence model has been developed to help computers work more efficiently with a wider variety of languages, such as languages that receive little attention from computer scientists, like African languages. The new model, developed by researchers at the University of Waterloo, specifically targets 11 African languages spoken by more than 400 million people. Advancements such as these show us not only how far artificial intelligence has come in terms of language but also how much more advancement is possible and the impact it can have on billions of people’s lives in the future.
How AI Impacts Academic Publishing
AI and NLP have the ability to accelerate internal publishing processes by working faster, being cheaper, and providing shorter turnaround times. Seen frequently in STM publishing, AI is increasingly being used for editorial functions, even going as far as to cover the writing and formatting of text.
As defined earlier, natural language processing aims to have computers understand languages. Programs are trained to recognize all aspects of language in an attempt to discern their meaning. As such, using natural language processing technology, publishers can automate editing and formatting tasks and instead focus on enhancing the content. Publishers can also manage submissions and even speed up the peer review process, which is a standard requirement in academic publishing. All of this allows publishers to maintain reasonable staff levels and production costs while speeding up the publishing process.
When you consider that manuscripts can go through multiple rounds of corrections along with copy editing, formatting, and proofing before finally moving through production to be published, AI and natural language processing technologies can be set up to do the work quickly. With pre-set grammar and formatting rules, manuscripts can be analyzed for quality. Furthermore, implementing these technologies can help automatically correct grammar and punctuation issues as well as the more involved issues that would require an editor’s attention. In this context, manuscripts that are of the highest quality can move straight through to being prepared for publication.
Taylor & Francis is one such publisher moving toward the use of AI, and they have positively expressed that utilizing this technology saves production time. Other publishers are using artificial intelligence and natural language processing for peer review, identifying plagiarism, and determining the originality of work by pulling
Overall, one of the most important advantages of AI in publishing is in its minimization of human errors.
Replacing Human Copy Editors With AI?
Understanding how natural language processing and artificial intelligence can satisfy so many aspects of the publishing process begs the question: Can it replace humans in the copy editing field?
An increasingly seen argument for AI is about how it can be used in place of humans. What does that mean for a task such as copy editing, where language professionals have been thoroughly trained and vetted for their ability to properly correct and enhance a document? For instance, the copy editors at eContent Pro have years of language experience under their belts and the ability to flawlessly edit a manuscript by correcting simple things like spelling and grammar along with more complex pieces like wordiness or inappropriate jargon. Capturing an author’s voice is not something that can be easily simulated, and, when left solely to a machine, the author’s style can easily be lost.
A simple reason why it would be difficult to replace human copy editors is the frequency in which language changes and evolves. Think of all the words Merriam-Webster adds to the dictionary each year. In 2021 alone, they added over 300 words and definitions. It would be nearly impossible for natural language processing technology to keep up with all of these changes in real time, making the human approach to copy editing a better option. Additionally, the way in which language is learned proves to be another downside of using AI for copy editing. Research shows that language is not necessarily directly taught but learned from experiences and the context in which the words are used. A similar process does not exist in artificial intelligence, which further slows the ability of machines to evolve with language.
That’s not to say there are not some pros to utilizing artificial intelligence and natural language processing technology in copy editing. The speed in which a computer can complete a copy-editing job may be faster than a human, but the quality may be lost. Artificial intelligence can be used as a supplement rather than a replacement, allowing the human editors to focus on making the content worth reading rather than focusing on the small grammatical discrepancies that may be present. Computers may be able to correct one’s spelling, but only a human can ensure your manuscript is logically organized and flows properly.
eContent Pro’s team of copy editors will ensure your manuscript is organized as they polish your work for submission. We always strongly advise authors to utilize copy editing services as it greatly increases your chance of acceptance into the publication of your choosing. While it may seem easy to use editing software or trust your writing software to underline all your mistakes so you can fix them, a human copy editor will provide the valuable corrections and feedback that publishers are looking for in a manuscript, bringing you one step closer to having your work published.
Limitations of Natural Language Processing
Though NLP certainly has its advantages, it still presents limitations that cannot be ignored. While human error is always a possibility, so is machine error. Contextual understanding of phrases with similar meanings, along with words having similar pronunciation but entirely different meanings that are often seen in the English language, can be problematic. While humans can easily differentiate the meanings, natural language processing models may not have this ability.
Text and speech errors can also create problems with natural language processing technology. A common example of this is autocorrect. How many times has a minor typo caused you to send a completely different message than intended due to the autocorrect feature changing your word? While common mistakes may be simpler to correct, intended meaning might not always be recognized. Additionally, spoken language functionality might not recognize mispronunciations, accents, or speech impediments.
Another major limitation is low-resource languages. As mentioned earlier in the case of African languages, many languages are overlooked in favor of languages that are more common and more widely used. This limits the availability of software in a more comprehensive range of languages for use by more people globally, thus reducing the effectiveness of natural language processing.
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