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What is natural language processing (NLP): Techniques and use cases

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Ever tried to learn a foreign language? Then you’ve got to second that comprehending sentences and contextualizing certain language structures can be a real challenge, especially for non-natives. Yet, we’ve come to an age where machines pull off a decent understanding of human language in both written and verbal forms: An age where we trust AI to proofread our emails, conduct web searches for us based on vocal prompts, or even generate a whole new piece of article, just like this one. Kidding! Or who knows? Now, for a machine to be able to put together an article like this, it would have to have natural language processing (NLP) at its core. Ready to know all the ins and outs of NLP? Get a feel for what we’re about to cover here:

  • What is NLP?
  • Natural language processing (NLP) techniques
  • What is it used for?
  • Open-source NLP libraries
  • Back to NLP’s impact and more

What is NLP?

As a branch of AI, NLP helps computers understand the human language and derive meaning from it. There are increasing breakthroughs in NLP lately, which extends to a range of other disciplines, but before jumping to use cases, how exactly do computers come to understand the language? Data.

Natural language processing (NLP)

Book chapters, reports, personal chats, IG comments, and tweets are all examples of textual data that often serve as training data for NLP models. The problem here: despite the ridiculous loads of data generated every minute on the net, the overwhelming majority is unstructured, which does not say a thing to your model. Unstructured data equals no data to a supervised learning solution. But today, NLP exists all thanks to the advances in computational power and, more importantly, text annotation (where sentence or text body parts are highlighted by different criteria, giving structure to your data). Annotated or labeled textual data is then fed into the model for it to iterate, extract features, and learn patterns. The true value of NLP, though, is better puzzled out through examples.

Natural language processing (NLP) techniques

NLP starts with data pre-processing, which is essentially the sorting and cleaning of the data to bring it all to a common structure legible to the algorithm. In other words, pre-processing text data aims to format the text in a way the model can understand and learn from to mimic human understanding. Covering techniques as diverse as tokenization (dividing the text into smaller sections) to part-of-speech-tagging (we’ll cover later on), data pre-processing is a crucial step to kick-off algorithm development.

While there exists a handful of algorithms for NLP (mostly encompassing machine learning, deep learning, and neural networks), each language task requires a different approach. The pool of these approaches, however, can be split into two major groups: syntactic and semantic.

Syntactic approaches

Anything syntax-specific can be found under this category:

Lemmatization: As one of the key techniques in NLP for data pre-processing, lemmatization is essentially reducing the word to its root word, also called a lemma. Unlike stemming, in lemmatization, this reduction from the tail of the word is not necessarily letter-level, meaning the algorithm can connect words based on the meaning. Take irregular comparatives and superlatives, for example. A lemmatization algorithm can identify that the root of less is little.

Stemming: Stemming, by contrast, although shares the definition with lemmatization holding up to the same word-reduction logic, would not spot the connection between less and little. It would just chop off one letter at a time, without getting to the essence of the word.

Morphological segmentation: By breaking the word into smaller morphemes (units) morphological segmentation extends its applications to speech recognition, data retrieval, machine translation, etc.

Part-of-speech tagging: Dealing with the syntactic structure, part-of-speech tagging refers to analyzing and interpreting grammatical units of the words, be they nouns, verbs, adverbs, and so on.

part-of-speech tagging

Parsing: By and large, parsing also refers to the grammatical analysis of the provided sentence, except here, sentences are assigned a structure to reflect how sentence constituents are related to one another. This is the reason why parsing often results in a sentence-level parse tree.

Tokenization: By dividing a sentence into smaller parts or setting sentence boundaries, tokenization allows for easier parsing later on.

Semantic approaches

Albeit limited in number, semantic approaches are equally significant to natural language processing.

Named entity recognition: This method allows to label parts of the text into relevant groups, be they names, places objects, etc. Grammarly, Siri, Alexa, and Google Translate all use named entity recognition as part of natural language processing to understand textual data.

named entity recognition

Word sense disambiguation: What word sense disambiguation does, humans do on an unconscious level. Some words do not have a single meaning and we, humans, fit them into the context without much effort. For a machine to be able to do the same, it has to identify which ‘sense’ or meaning of the word is triggered in a given context—all thanks to word sense disambiguation.

Natural language generation: As inferred, natural language generation (also NLG) is about using input data or databases to conduct semantic analysis and deliver human language text.

What is NLP used for?

Current NLP-based AI solutions cover a wide range of applications often built using Python, PyTorch, and TensorFlow. Some of the fields that benefit most from NLP include the following:

  • Invoice analysis: Extract recurring entities to understand how payments align with respective request dates.
  • Clinical documentation and disease extraction: Analyze electronic health records and reporting to extract diagnoses and treatment outcomes and eventually automate clinical trials (e.g., Amazon Comprehend Medical).
  • Talent recruitment: Identify target skills from sets of dozen resumes and shortlist relevant candidate profiles.
  • Customer experience: Conduct sentiment analysis on sample reviews to develop tailored e-shopping experiences.
  • Chatbots: Understand the natural human language, engage online visitors, and provide instant customer service.
  • Grammar accuracy checks: Deploy NLP to extract syntactic insights and suggest changes (e.g., Grammarly).
  • Language translation: With NLP, your model can also learn to translate chunks of text (e.g., Google translate).
  • Personal voice assistants: Search items on the web based on verbal commands (e.g., Siri and Alexa).

As accessible as it may sound, building an NLP model is far from being full-on straightforward.

Part of this difficulty is attributed to the complicated nature of languages—possible slang, lexical items borrowed from other languages, emerging dialects, archaic wording, or even metaphors typical to a certain culture. If perceiving changes in the tone and context is tough enough even for humans, imagine what it takes an AI model to spot a sarcastic remark. And that’s only the tip of the iceberg.

Open-source NLP libraries

When developing an NLP model, choosing the right library is essential to achieving your target outcomes. Below we’ll cover several of the most popular NLP libraries for you to explore.

Stanford Core NLP

Stanford Core NLP: Great fit for processing large chunks of data with potential scalability. With this library, you can scrap information from open sources, conduct sentiment analysis, part-of-speech tagging, named entity recognition, and much more.

Apache OpenNLP

Apache OpenNLP: As a machine learning-based toolkit for the processing of natural language text, OpenNLP supports a range of NLP tasks (named entity recognition, sentence segmentation, tokenization, parsing, part-of-speech tagging, and so on). It also uses Java NLP libraries with Python decorators, just like Stanford Core.

NLTK (Natural language toolkit)

NLTK (Natural language toolkit): NLTK is an excellent match for simple text analysis and can be tangibly slow for complex applications. The toolkit has an engaging discussion forum too and provides a suite of text processing libraries for tokenization, tagging, classification, stemming, semantic reasoning, parsing, etc.


SpaCy: Compared to NLTK, SpaCy offers a more streamlined experience, partly also because of its API. It has all functions combined at once, contains word2vec and doc2vec, and is a recommended choice for syntactic analysis applications, particularly named entity recognition, conversational user interface optimization, etc.

Back to NLP’s impact and more

In this article, we’ve talked through what NLP stands for, what it is at all, what NLP is used for while also listing common natural language processing techniques and libraries. What else to take off of this piece? NLP is a massive leap into understanding human language and applying pulled-out knowledge to make calculated business decisions. Both NLP and OCR (optical character recognition) improve operational efficiency when dealing with text bodies, so we also recommend checking out the complete OCR overview and automating OCR annotations for additional insights.

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