What is Natural Language Processing, and how does it work?
All of our daily interactions such as texting, emailing, socialising and expressing feelings amount to something called natural language, and without the ever-growing field of Natural Language Processing (NLP), computers would not understand any of it.
The question is: how do we teach a computer to understand and effectively generate natural human language?
To provide better insight about NLP we have spoken to Helen, Jr. NLP Engineer at Marketer.
Could you explain what NLP is?
Natural language processing uses a combination of computational linguistics, machine learning and AI to understand spoken or written natural human language. It can be roughly divided into two categories:
- Natural language understanding
- Natural language generation
Natural language understanding is the process of analysing and making sense of natural text or speech, which is a challenge because the way we actually speak is very different from the conventional grammatical structures the machine might expect to see. Natural language generation is when the system generates new text based on the original text, for example, a chatbot providing an answer or a machine translator generating a translation.
What does your role as a Jr. NLP Engineer include?
I’m working on an NLP system that integrates features like text analysis and text generation into our automated marketing solutions. My days consist of developing and testing new features as well as experimenting and researching, as the field of NLP is constantly evolving.#nbsp;
In what ways can you use NLP?
NLP is used for things like spam detection, machine translation, chatbots, virtual assistants and text summarization. It is a powerful and widespread technology making its way into more and more services around the world.
Are there any challenges in the field of NLP?
One of the main challenges in the field of NLP is ambiguity; the fact that one specific expression can have multiple meanings. Humans solve this problem in everyday communication by looking at the context of the given word or expression. Teaching computers about context is not an easy task, as they lack general knowledge and the ability to automatically interpret different meanings. Another big challenge is the frequent use of idioms, dialect, sarcasm and slang within natural text and speech. These elements easily make sense to us but are impossible for a computer to understand without the appropriate training. However, I actually think that the challenges with the field are also what makes it interesting.