NLP & AI

NLP in Practice Chatbots Translation and Sentiment Natural language processing helps chatbots understand messages, switch languages, and read emotions. In real apps, teams manage translation quality and tone across many markets. This post offers practical ideas to blend translation and sentiment into a smooth chat experience. Translation in practice Translation happens in two steps. First, user input is translated to a common internal language the bot can process. Then, after the bot replies, the text is translated back to the user’s language. A short glossary keeps product terms and tone consistent. A translation memory speeds up work by reusing past translations. For critical flows—checkout, support, or order updates—human editors should post-edit MT outputs to ensure accuracy. Keep content separate from code so translators can update phrases without touching logic. ...

NLP Pipelines: From Data to Deployment A successful NLP project follows a clear path from data to a live service. It should be repeatable, explainable, and easy to improve. The work is not just about building a model; it is about shaping data, choosing the right techniques, and watching the system perform in the real world. With thoughtful design, teams can move from ideas to reliable outcomes faster. Data collection and labeling: Gather text from relevant sources such as customer reviews, chat logs, or open datasets. Define labeling guidelines to keep annotations consistent. Start with a small, high-quality seed set to test ideas before scaling up. Clear provenance helps reproduce results later. ...