More than 12 NLP Techniques, Methods, and Approaches

In natural language processing, or NLP, techniques focus on tasks such as processing language, understanding meaning, summarizing text, and more. 

In this post, we’ll take a look at some of the top techniques used in NLP.

A list of NLP Techniques, Methods, and Approaches

NLP focuses on two goals: understanding or creating human language. 

Since generative AI, or AI that creates original content, is still new, we’ll focus on the first aspect of NLP – analyzing and processing existing texts.

Here are a few of the most common and popular texts in this field:

Topic modeling is a technique designed to pick out topics within the text. It accomplishes this by assigning words to a topic, then looking for those words in the text. When the right number of words are found then it will assume that this is a topic within the text. For instance, words such as “workplace,” “employee,” “organization,” and “employer” would all belong to a topic such as “business.” 

Text summarization is a technique designed to, unsurprisingly, summarize a text. To this end, the NLP application will extract specific parts of the text, then, through a process of abstraction, generate a more concise version of the text.

Sentiment analysis refers to a very simplified analysis of emotions. Generally, the emotions are positive, negative, or neutral. Those three sentiments can then be scored numerically and used for different business purposes, such as marketing and brand monitoring.

Part-of-speech tagging, or grammatical tagging, is a technique used to assign parts of speech to words within a text. In conjunction with other NLP techniques, such as syntactic analysis, AI can perform more complex linguistic tasks, such as semantic analysis and translation.

Named entity recognition is a task used to identify certain terms within the text such as people, location, the names of companies, and so forth.

Aspect mining identifies an aspect or all of the “aspects” within a text, such as opinions. Used alongside the other techniques covered here, such a sentiment analysis, aspect mining can offer an analysis of attitudes towards different topics covered in the text.

Syntactic analysis takes grammatical tagging one step further. Rather than identifying the individual parts of speech that words belong to, syntactic analysis techniques analyze the sentence structure by evaluating how words relate to each other.

Semantic analysis is designed to extract the meaning of a text. This is achieved by “learning” what the individual words mean individually, what they mean in a specific context, and how they relate to each other within the text.

Tokenization is the process of subdividing text into smaller units, such as words or sentences.

Lemmatization and stemming refer to transforming words into their base form, such as removing “-ing” from the end of a word to find the dictionary form of the word.

Latent semantic indexing is designed to find words and phrases that occur often in conjunction with each other.

Clustering means grouping similar documents into a text and then sorting them based on relevance.

Matrix factorization refers to quote unquote latent factors designed to separate a large matrix into a smaller one.

In general, NLP techniques are not useful in and of themselves. 

Tagging parts of speech, for instance, is only valuable if it is used in conjunction with other techniques covered above, such as syntactic analysis and semantic analysis. When the right techniques are combined under one hood, they can be used to create innovative digital products and services, as we’ll see next.

What are the use cases of NLP in business?

Here are just a few ways NLP is used in business.

Translation. Translation apps analyze, among other things, the grammatical structure and the semantics of a text in order to discover its meaning. That meaning is then translated as accurately as possible from one language into another, using apps such as Google Translate.

Search query relevance. Semantic search refers to the use of semantic analysis to understand web searchers’ intent when they perform web searches. This can improve search relevance, the search engine user’s experience, and, ultimately, the value of the search engine.

Chatbots. Chatbots are software programs that use human language to interact with people. They are often used in areas such as customer service, employee self-service, and technical support.

Brand monitoring. To better understand how their brand is being perceived, some companies will use brand monitoring tools to analyze sentiment online. This often involves data mining the web, social media, reviews, and other places that would offer insight into people’s perception of their brand.

Language generation. Although the technology is still new, generative AI is already being used to create original text. One of the most promising use cases is in marketing, where automated copywriting software can be used to write ads, landing pages, and other short-form copy.

These are just a few of the many examples of how NLP can be used in a business context. In the years to come, we can expect to see this technology become more sophisticated and more common. For businesses, these types of automation platforms can generate a significant advantage in the market, which suggests that early adopters will be rewarded.