Making machines understand human language is not a simple task. It is a multi-stage process. First we need to clean the unstructured data, then pre-processing, tokenization, normalization, typographical errors, Named Entity Recognition (NER), and dependency parsing. The end result is Natural Language Processing (NLU) which has many real life applications of the everyday world which we are actively pursuing:
- Sentiment Analysis is a useful technology that businesses can apply in social media, customer reviews, and customer support. A common use of sentiment analysis is reputation management. Understanding how customers perceive your products or services is extremely useful in the day and age of social media. Sentiment Analysis can also play an essential role in customer support. It can monitor opinions and emotions in real time, allowing more emotionally intelligent customer support.
- Named Entity Recognition is about breaking raw text up and identifying relevant entities. We are generating a mind-boggling amount of online content on a daily basis. NER can automatically scan a large amount of content in a short amount of time and reveal what's relevant. If we run customer's request through the NER API, it pulls out the entities "Calvin Klein", "jeans," "boot cut," and "size 10.” They can then be used to categorize the customer request and assign it to the relevant department or even automatically process. As you can see, it's crucial for the NER to be able to identify "Calvin Klein" as a brand instead of a person, and "boot cut" as a style instead of "boot" and "cut" into separate entities.
- Automatic question and answer generation allows dynamic and most up-to-date FAQ for customer support. FAQ has traditionally been nothing more than a static sets of Q&A sitting on a static website sitting around waiting for visitors. Leveraging NLU as foundation, FAQ becomes an interactive experience. With AI-powered chatbot interfacing customers, NLU can understand customer's questions and automatically extract accurate answers from a pool of documents.
NLU is perfect for solving classification problems. The task of customer service, restaurant reservation, travel planning can all be easily solved by our AI. The goal is for us to spend less time doing manual work and delivering quantifiable return with artificial intelligence.
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