Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on teaching machines to understand and interpret human language. NLP has many practical applications, from chatbots that can answer customer service questions to sentiment analysis tools that can gauge public opinion on social media. But how does NLP actually work? Let’s take a closer look.
- Tokenization The first step in NLP is to break down a sentence into its component parts, or tokens. This involves separating individual words and punctuation marks. For example, the sentence “I love pizza!” would be tokenized as “I”, “love”, “pizza”, and “!”.
- Part-of-Speech Tagging After tokenization, NLP algorithms assign each word a part-of-speech tag, such as noun, verb, adjective, or adverb. This helps the algorithm understand the grammatical structure of a sentence.
- Dependency Parsing Dependency parsing involves analyzing the relationships between words in a sentence. This helps the algorithm understand the meaning of the sentence. For example, in the sentence “John gave Sarah a book”, dependency parsing would reveal that John is the subject, gave is the verb, and book is the object.
- Named Entity Recognition Named entity recognition involves identifying entities in text, such as people, organizations, and locations. For example, in the sentence “I visited New York City last week”, named entity recognition would identify “New York City” as a location.
- Sentiment Analysis Sentiment analysis involves analyzing the emotional tone of a piece of text. This is useful for gauging public opinion on social media, for example. Sentiment analysis can be done using machine learning algorithms that are trained on large datasets of labeled text.