A Guide to Natural Language Processing

Natural language Processing (NLP) is the automatic manipulation of natural human language using computational linguistics and artificial intelligence. As per Wikipedia, it  is a sub-field of linguisticscomputer scienceinformation engineering, and artificial intelligence. In simple terms, a computer can analyze, understand and derive meaning from human language, in a simple way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as summarization, translation, parts-of-speech tagging, sentiment analysis, named entity recognition, stemming, relationship extraction, speech recognition, and topic segmentation.
Human language is seldom precise, or plain-spoken. To understand human language it is important to understand not only the words, but the intent and concepts to link together to create meaning. The ambiguity of language may be one of the easiest things for the human mind to learn, but it makes NLP a difficult problem for computers to master.

Basics of NLP


Although NLP can be tricky, but there are several programming languages like Python and R, and other techniques that can handle the challenges.
The basic vocabulary used in NLP:

  • Syntactical Analysis: It is used to assess how the natural language aligns with the grammatical rules. Some terms used around syntax are:
    o Bag of Words: It allows you to count all words in a piece of text. The frequency or occurrence of a word can be used for training a classifier disregarding grammar and order.
    o Tokenization: The process of segmenting running text into tokens of words and sentences, removing punctuation and certain characters.
    o Stop Words: These are a set of common language articles, pronouns and prepositions such as “and”, “the” or “to” in English and should be cleared. It can be safely ignored by carrying out a lookup in a pre-defined list of keywords, freeing up database space and improving processing time.
    o Stemming: It refers to the process of slicing the prefix or suffix words with the intention of removing affixes. Python and R languages have different libraries containing affixes and methods but it also presents some limitations.
    o Lemmatization: It is the process of reducing a word to its base form and grouping together different forms of the same word.
  • Semantic Analysis: It refers to the meaning that is conveyed by a text by creating correct structures. Some techniques used in semantic analysis are:
    o Named entity recognition (NER): It involves determining the parts of a text that can be identified and categorized into preset groups.
    o Natural language generation (NLG): It involves using databases to derive semantic intentions and convert them into human language.
    o Parsing: It involves undertaking grammatical analysis for the provided sentence.
    o Part-of-speech tagging: The process of identifying the part of speech for every word.

Google Translate is used by 500 million people every day to understand more than 100 world languages.

Use Cases of NLP

  • NLP algorithms, typically based on machine learning (ML) algorithm, can provide the developers with the vital tools needed to create advanced applications, and prototypes. NLP can help with wide range of fields of applications and tasks, such as:
  • By extricating information from sources like social media platforms, organizations can determine what customers are saying about a service or product. This process of sentiment analysis can provide a lot of information about their choices and can thus act as decision drivers.
  • Apple’s Siri and Amazon’s Alexa are some of the best examples of intelligent speech-driven interfaces that use NLP to respond to verbal prompts.
  • Companies like Yahoo and Google, filter and classify your emails using NLP by analyzing text in emails that flow through their servers and prevents spam mails to enter your inbox.
  • NLP can be used in financial trading by creating an algorithm to track news, reports and comments about possible mergers between companies and generate massive profits.
  • NLP when paired with voice recognition technology has propelled the chatbots to a new level. Gartner predicts, chatbots will account for 85% of customer interactions by 2020. They already have been used in businesses for a long time now and NLP enabled smart bots will make it even better.
  • In healthcare industry, NLP can be deployed with predictive analysis to aid in identification of high-risk patients as well as improvement of the diagnosis process.
  • Machine translation is a huge application for NLP that allows us to overcome barriers to communicating with individuals from around the world.


Know more about DAAS Labs NLP offerings here.

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