If there’s one thing there’s plenty of in artificial intelligence (AI), it’s inside terminology. One commonly used phrase is natural language processing (NLP) – a subset of AI that teaches machines how to understand human language. Common applications include chatbots and voice assistants, such as Apple’s Siri or Amazon Alexa.
Just like it’s difficult for people to learn a language, NLP isn’t easy either. Human speech is filled with nuance and subtlety, unique pronunciations, and multiple dialects. To help AI sort through the variances, an NLP subset called natural language understanding (NLU) works with the many complex ways in which we communicate. The transition from NLP to NLU is one of the main research areas that are currently hot in language AI.
Examples of Using Natural Language Processing
Voice assistants and smart speakers
To power Siri, Alexa, and similar voice assistants, NLP filters the spoken words, digitizes them into a machine-readable format, analyzes their meaning, then determines a person’s needs based on data and algorithms.
Chatbots heavily rely on NLP to mimic human conversation. They process the words people use in conversation on vocabulary and grammar-based levels in order to better understand the context and answer questions accordingly.
Context understanding and intention recognition help chatbots use language more like people.
- Context understanding combines conversational elements like location and time to get a broader understanding of what somebody’s talking about. Just as real people have to know the context in order to fully comprehend human conversation, chatbots need context understanding to communicate accurately as well.
- Intention recognition extracts relevant information from each sentence or word in order to better understand their associated intent or meaning. This technology also enables chatbots to extract and understand multiple things within a single sentence and respond correctly – even when conversations are long or complex.
Types of Training Data Required for Natural Language Processing
Entity extraction and tagging
Entity extraction takes “units of information” from text or unstructured data then structures them. These units can include proper nouns, such as the name of a person, organization, or place. They can also identify amounts or numbers, like the time, the date, a price, or a percentage.
Semantic annotation helps AI systems evaluate search results. Companies are always looking for ways to improve search relevance so that search engines can better drive customers to their products. Unfortunately, product descriptions can vary widely from one place or the other (like a company’s catalog versus its website) and these details can often be inaccurate as well. Semantic annotation tags product names and search queries to improve search engine results.
When companies can annotate the best category for each product, eCommerce-driven search becomes easier, faster, and much more accurate.
Linguistic annotation evaluates the subject of a sentence. From sentiment analysis for social media to question-answering chatbots, linguistic annotation is used in a range of fields, including essentially everything involving text analysis.
The Future of Natural Language Processing
Moving forward, it’s important for the AI industry to develop NLP tools that can maintain the natural flow of conversation and respond to people appropriately. But developing human-like chatbots and voice assistants requires high-quality data. The more training data a company has, the better its NLP engines will be able to perform, improving AI customer experience overall.