What is Natural Language Processing and its Use Cases?

Olivia Brookhouse    27 March, 2020

Our ability as humans to use words to communicate intelligently is what defines us as a species. There are 7000 languages in the world connected to different countries, cultures and groups of people. Computers with the development of Natural Langauge Processing (NLP) are now also becoming masters in linguistics to improve global communication and business processes.

The incorporation of Artificial Intelligence into business practices spans across all sectors and is a term many of us are familiar with. However, how well do you know Natural Language Processing (NLP) and its applications for businesses?

What is NLP?

Natural Language Processing is the branch within the field of computer science, linguistics and artificial intelligence that is responsible for the study and development of techniques that enable computers to understand and process human language. It is not only about understanding some words, beyond that NLP aims to understand the meaning of an idea and the context.

NLP has many use cases to innovate big and small companies alike. This includes powering efficient chatbots for customer service, providing sentiment analysis from social media posts to find out consumer habits, improving processing and understanding of voice searches, scanning and summarizing documents, reading CV’s etc. Any lengthy process that involves text analysis, AI can be an incredibly useful assistant to make work more efficient.  

The technology behind it

There are 6 important steps which allow computers to understand the meaning behind words to provide actionable insights. Python NLTK can run all these different stages required for NLP.

Tokenization – process of breaking strings of text into tokens, which are small structures that can be analysed separately. For example, a sentence can be broken down into individual words to help make sense of how each word plays in the meaning of the phrase as a whole.

Figure 0: Tokenization explained (Source)
Figure 0: Tokenization explained (Source)

Stemming – process which reduces words into their base form or root form. For example, the word ‘perfected’ would be normalized to its root word – perfect. This process is necessary to simplify the words, but sometimes will cut words to a shorter word that might not exist.

Lemmatization – like stemming, Lemmatization generates the root from of the inflected words but is more accurate. Lemmatization will always create an actual language word when it cuts the word down. It also groups together different inflected forms of words to map them into one common root. So during lemmatization, the computer should map gone, going and went to go.

Figure 1: Lemmatization and stemming explained (Source)
Figure 1: Lemmatization and stemming explained (Source)

POS tags – Indicate how a word functions in a sentence, categorizing them as an adjective, noun, adverb, subject etc. In different contexts the same word will have different POS tags. For example, google is a proper noun but it can also function as a verb, for example in the sentence “google the football score”.

Named entity recognition – is the process that aims to identify the name entities within a sentence and categorize them. Categories can include, person, organization, location, time. It does this in 3 steps:

  • Noun phrase identification
  • Phrase classification
  • Entity disambiguation
Figure 2: Named entity recognition process (Source)
Figure 2: Named entity recognition process (Source)

Chunking – process of picking up individual pieces of information and grouping them into bigger pieces which gives meaning to the text, identifying which words need to be chunked together allows greater understanding of meaning.

Figure 3: Chunking explained
Figure 3: Chunking explained

NLP and NLG

There is often a confusion between these two terms, Natural Language Processing and Natural Language Generation. Whilst Natural Language Processing (NLP) is the process in which computers read language, understand it and convert it into structured data which can be used for analysis and training, Natural Language Generation (NLG) is what happens when computers write language. NLG processes turn structured data into text to help automate and speed up text writing within applications.

These two processes often work hand in hand to not only help businesses process and analyse large quantities of language data (NLP) but to also automate writing processes (NLG).

Use cases of NLP

Medicine

The development and need for Telemedicine depends heavily on Natural Language Processing to accurately diagnose diseases. NLP scans data from hundreds of patients to build accurate symptom profiles for diseases. This can also help in the prevention of pandemics. NLP is also within medical chat bots to speed up internal processes, understanding individual cases in order to refer patients to the correct doctor.

Finance

Traders are increasingly using NLP to provide social media sentiment analysis to better understand market trends. NLP can perform spell checking and document summarization to speed up internal administrative processes.

Marketing and Advertising

NLP makes many marketing processes more efficient by performing advertisement matching and personalisation, copy writing and key word identification. Also, sentiment analysis can provide a lot of information about customers choices and their decision drivers.

Customer service

Natural language processing and generation play a large role in customer service to provide a 24/7 service. Accurate chatbots and voice assistants must be able understand what we are saying but develop a helpful response.

Recruitment

NLP is also being used in both the search and selection phases of talent recruitment, identifying the skills of potential employers before they become active on the job market can give recruiting companies the edge. NLP can also be used to scan CV’s in seconds to extract key insights about candidates

Entertainment

Within platforms like Movistar home, Aura, the voice driven assistant uses NLP to respond to vocal prompts, help find and suggest content and many other functions.

NLG is also being used to generate its own content, including art, films, music and novels, although alot of its work is questionable.

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