Natural Language Processing · NLP
Natural Language Processing
What Is Natural Language Processing?
- The field of computers handling human language
- The foundation of translation, summarization, and chatbots
- The key to automating language work
What NLP really changes is the unit of processing for work that only people reading could do. 30,000 call recordings, 2,000 contracts, and a daily pile of customer feedback were lumps with no value until someone scanned them by eye. Add NLP, and those lumps become data you can classify, search, and aggregate. So the payoff shows up first not in 'writing plausible answers,' but in sentences buried in inboxes and support histories surfacing as a single line in the weekly report.
When budgeting, it pays to separate understanding (NLU) tasks from generation (NLG) tasks. Understanding tasks with a right answer, such as classifying inquiries, detecting sentiment, and extracting named entities, can run on relatively small models, and you can measure accuracy with numbers. Generation tasks such as summaries and first drafts have no single right answer, so human review stays in the process. Korean is an agglutinative language that attaches particles and endings, so it needs processing that treats 'refund-SUBJ', 'refund-OBJ' and 'please refund' as one group, and without an internal glossary of company terms and abbreviations, even the same model performs far worse. Also note that RAG, which answers from internal documents, is not a substitute for NLP: the retrieval step sits on top of NLP quality.
The most common failure in practice is taking an accuracy figure at face value. At 92% accuracy, out of 10,000 inquiries a month, 800 end up in the wrong department. If refunds or complaints are among them, response delays turn directly into formal complaints and cancellations. The claim that “a large model means no labeling is needed” is only half true. Without ground-truth data for review, you have no basis for judging whether a model swap made things better or worse. The safe sequence is to first build an exception desk that hands misclassifications to people, then raise the automation rate step by step.
John KimInstructor John Kim, author of B2B Marketing Design and CEO of Nanum Consulting
Seolyeong ParkGenerative AI and AX training specialist with 17 years of hands-on experience and 200+ lectures, helping non-technical staff put AI to work right awayA long-established field that began with machine translation research in the 1950s. It evolved from rule-based to statistical to deep learning (Transformer) approaches, and the arrival of large language models brought a leap in performance.
Consider a hypothetical case. Company A, a home appliance rental business in the Seoul metropolitan area, has 480 employees and 32 support staff, and receives 12,000 customer inquiries a month. Agents read and sorted 5,000 of these KakaoTalk and email messages by hand, so the first response took 14 minutes on average, and 210 cases a week were re-routed after landing on the wrong team. Over four months, they added NLP classification and summarization.
- Kim, head of the customer service team, pulled the past three months of 5,200 inquiries and labeled them into 14 types such as 'installation delay,' 'billing question,' and 'cancellation.' The first rule was set up front: "Name the categories in the words agents actually use."
- When an external model was given a 300-term in-house abbreviation dictionary and run as a pilot classifier, accuracy reached only 78%, and 'installation delay' and 'cancellation' kept getting mixed up. Adding 400 more example sentences for those two types raised accuracy to 89%.
- After automatic classification and a three-line summary were added to KakaoTalk and email inquiries and shown at the top of the support screen, this manager said, "I no longer have to reread the full history from the beginning."
- Cases with a confidence score below 0.8 were not auto-assigned. They went to a queue reviewed by two senior agents. About 60 cases a day were diverted this way, and misrouting dropped noticeably.
- Four months later, average first response time dropped from 14 minutes to 5 and re-routed tickets fell from 210 to 48 per week, while the 320 hours a month once spent on classification and summarization shifted to improving support quality.