NLP, or Natural Language Processing, is a part of the Conversational AI (artificial intelligence) tech stack that allows machines to understand and generate human language. It combines Natural Language Understanding (NLU), Natural Language Generation (NLG), and Machine Learning algorithms to process text and speech data. NLP relies on speech recognition, machine translation, and sentiment analysis to truly understand what a human being means. Broadly speaking, NLP is a translator that helps human beings and machines to communicate.
How NLP works?
Even though NLP is like a translator between humans and machines, that statement is very simplistic. Rather it’s a cluster of technologies that come together and enable this human and machine dialogue. In this section, we have used the phrase “Can you bank on that?” to illustrate what these technologies do.
- Natural Language Understanding (NLU): NLU is a process that focuses on understanding the intent and context behind the user’s words. In our sentence “bank”, by definition is a financial institution, but NLU understands it contextually. The computer knows that “bank” in this specific statement means “rely on” or “trust.”
- Semantic Analysis: Semantic analysis looks at the relationship between a user’s words to understand broader meaning. For the computer, the word “bank” is related to “on that” rather than being a financial term. Semantic analysis knows that “bank on” is a phrasal verb that means “to depend on”.
- Word Sense Disambiguation: Our sentence and its understanding are quite simple but this process is crucial when the machine is resolving words with multiple meanings. In our case word sense disambiguation helps the system to choose “bank” meaning trust or reliance instead of a place to deposit money. With more complex sentence and paragraph changes within a text, this process is invaluable.
- Machine Translation: Machine translation is a technology that ensures the meaning of a sentence doesn’t change. When we translate our example sentence into French it would be translated into “Peux-tu compter là-dessus?” where “computer” means “rely on” rather than “banque” (which means bank). If we were to ask an AI to paraphrase this sentence, it would retain the meaning of the sentence.
- Natural Language Generation: NLG is another technology that enables the machine to generate a human-like response once the machine understands the response. In this case, the machine can reply with “Yes, you can count on that.” making the interaction feel natural and conversational.
- Speech Recognition: Another technology that helps NLP understand the user’s words even when they are spoken. Speech recognition uses sound waves to convert speech into text so the rest of the NLP stack can work on it. It transcribes spoken language into written text.
In an AI system, NLP is one of the most crucial layers that enables machines to understand humans. However, it’s not the only one. In a conversational AI stack, the user’s input often goes through multiple layers of processing to generate factually and contextually accurate responses. In the future, we intend to do a larger post covering each of these layers.
How to Optimize NLP
To improve NLP models a combination of machine learning methods, deep learning, and statistical methods are used to improve accuracy in understanding and generating language. Here are:
- Machine Learning: NLP uses machine learning to learn from huge amounts of text and speech data, to understand human language.
- Deep Learning Models: These models, powered by deep learning methods, process and learn from large datasets to improve language processing.
- Sentiment Analysis: NLP can be optimized to detect emotions and opinions in text to get insights into customer feedback and user sentiment.
- Named Entity Recognition (NER): This NLP task identifies and classifies specific entities (e.g., names, places) within a text to extract meaningful information from unstructured data.
Why does NLP matter?
NLP is important in human-machine interaction. It powers applications like virtual assistants, machine translation, and speech recognition software to automate human communication and extract valuable insights from language data. At Phonely, we rely on this tech to power our AI agents. Here are some of the benefits of NLP:
- Better Human Interaction: By making machines understand and generate human language NLP improves human technology interaction.
- Automation of Language Tasks: NLP handles many NLP tasks such as text classification, machine translation, and sentiment analysis, automating tasks that would otherwise require human intervention.
- Scalability and Speed: NLP systems can process large amounts of text and speech data in real-time making it scalable for industries that deal with large data.
Conversational AI, the one that understands human language and responds to it, would not be a reality without NLP. Combined with other technologies like NLU, NLG, LLM and machine learning; NLP today powers virtual assistants (Siri, Google Assistant) and many translation tools.
