Artificial intelligence (AI) encompasses multiple types of machine learning approaches, including computer vision, natural language processing (NLP), audio intelligence, and so on. As the name suggests, NLP is the branch of AI that centers on language and teaching computers how to understand and use it in a natural, human-like manner.
Organizations use natural language processing for chatbots, digital marketing, sentiment analysis, and more. If you have ever used the chat feature on a website, chances are that for at least part of the conversation, you were interacting with a chatbot that uses NLP.
What Is Natural Language Processing?
Natural language processing techniques are used to extract insights from unstructured text. It works by using training data to learn from previous conversations, email exchanges, chat text, and more. Based on speech patterns, frequent responses, and other points of reference, the machine learns how to answer future questions.
Because so much language data already exists in the world, NLP is a natural fit for transfer learning, which is a type of training model that uses the existing knowledge from a pretrained model as a starting point. The Transformer deep machine learning model includes:
- Preprocessing: Text segmentation based on the type of language being learned
- Sentence segmentation: Definition of individual sentences even if there is no punctuation
- Tokenization: Separation of each word in the text into individual tokens
- Parts of speech tagging: Classification of each token into the appropriate part of speech
- Lemmatization: Identification of the root word for each token
- Identification of stop words: Removal of words that don’t add value to the meaning of the sentence
- Dependency parsing: Assigning syntactic structure to the sentence
- Named entity recognition: Extraction of text into predefined categories
- Coreference resolution: Identification of the words that refer to the same entity
Natural Language Processing Techniques
People use language in countless ways, and these various NLP techniques are some of the methods used to get closer to human language.
1. Named Entity Recognition
Also known as text or entity extraction, named entity recognition identifies named entities in unstructured text. These could be names, dates, cities, and so on. The information is used to provide context for text, and potential applications include recommending content, analyzing feedback, and tagging content.
2. Sentiment Analysis
Brands can learn a lot from the unstructured text in reviews, social media profiles, and other online content. Sentiment analysis uses this data to analyze how customers feel about products, companies, events, news, and other information.
3. Text Embeddings
After text has been tokenized, words or phrases can be mapped to vectors, which allows the machine to analyze the information and compare words that are semantically similar across multiple languages. This NLP technique can be used to support sentiment analysis, name matching, news analysis, and content filtering.
4. Machine Translation
This technique translates from one language to another but goes beyond substituting words and phrases to include adapting sentence structure and translating idioms. It can be tailored to specific industries such as government, legal, healthcare, and so on.
5. Dialogue and Conversations
Humans communicate differently when writing and speaking. When machines learn to communicate in a more natural way, chatbots and other tools are more widely accepted.
6. Text Summarization
This technique pulls out the main points in a document to shorten long passages of text. It can either extract exact text or rephrase it, providing the benefit of reducing reading time to support more efficient research.
7. Aspect Mining
Aspect mining identifies common elements or concepts in multiple sources of unstructured data. Similar to sentiment analysis, it can be used to analyze reviews, social media content, and other sources to identify common themes and opinions.
8. Topic Modeling
Used to recognize common topics that occur in a set of documents, topic modeling can be used at the most basic level as a text-mining tool. It can also be employed to interpret genetic information, organize historical documents, and more.
Natural language processing offers many benefits across multiple sectors, including healthcare, academic research, marketing, and more. With the Skyl.ai universal platform, you can develop and execute your NLP project using one of our workflow templates. This means you don’t need to have an in-house data scientist, and you can coordinate every step of the project—from data collection to monitoring—from one place. If you’re curious about how a machine learning project works, check out our machine learning checklist.