Latest developments in Natural Language Processing (NLP)

Ever wondered how robots and machines perceive a command given to them?

Well, Natural language processing (NLP) gives them the ability to read, understand, and deduce useful information them from human languages. There are many applications of natural language processing including this.

As defined by Wikipedia, Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language. It talks about how to program computers to process and analyze large amounts of natural language data. NLP finds numerous applications in today’s world with major ones being in chatbots, sentiment analysis, and market intelligence.

Following are some of the major trends and advancements and some NLP examples that have dominated AI and the tech world in recent years-

  • Business Intelligence– There is a parallel connection between business intelligence and NLP. NLP facilitates the user’s interaction with complicated databases.
    • Using NLP companies gain product information like marketing and sales information, customer service, brand notoriety and the present talent pool of a company.
    • Another popular method of NLP used in BI is opinion mining. It uses NLP to extract customer sentiments from their reviews and ratings.
  • Semantic modelling- Semantic analysis is the process of relating syntactic structures, from the levels of phrases, clauses, sentences, and paragraphs to the level of the writing as a whole, to their language-independent meanings. Its goal is to draw the exact meaning from a text. It processes the logical structure of the text and identifies the most relevant elements in the text. It also understands the relationships between the elements of the text which is used in further analysis.
  • Chatbots– Natural Language Processing in AI is very popular. Atleast one-fourth of the organizations will have chatbots or visual customer assistants or some other type of NLP included in their customer service system by 2020. Chatbots learn the semantic relations, understand the objective of the questions asked, and then automatically perform the filtration and organization necessary to serve a relatable and significant answer, rather than simply showing the data. For instance, Microsoft’s Cortana is helping many small and large-scale businesses do research and process data by voice.
  • Human-machine interaction- One of the most common examples of usage of NLP in human-machine interaction is spam detection where emails undergo a process of getting filtered administered under NLP algorithms based on whether it is spam or not.
  • Deep learning for NLP– Deep learning techniques like Recurrent Neural Networks are used to get accurate results after analyzing the data.
  • Supervised and unsupervised learning– Natural Language Processing in machine learning is used for text analytics where statistics identify sentiments, expressions, and aspects of speech.



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Let us discuss the supervised and unsupervised learning aspects of NLP in more detail below since they house some of the most popular techniques.

In supervised learning, a set of text documents are tagged with examples with the machine of what is to be searched. These supplied examples are used to train the model which is later supposed to analyze the unlabelled or untagged text. Some of the most popular supervised learning NLP techniques are Support Vector Machines and Neural Networks. (A good read – Natural Language Processing (NLP) for Machine Learning)

In unsupervised learning, the model is trained without pre-tagging. Clustering, Latent Semantic Indexing (LSI), and Matrix Factorization are some popular techniques in unsupervised learning NLP.

  • In clustering, a similar types of data is grouped into sets and later sorted based on relevance using algorithms.
  • LSI on the other hand involves identifying words and phrases that frequently occur in the given text.
  • Matrix Factorization is different from the other two as it deals with breaking larger matrices into smaller ones using latent factors i.e., similarities between two or more items.
  • Reinforcement learning- Reinforcement learning along with supervised and unsupervised learning forms the three basic paradigms for ML. It allows machines and software applications to determine the ideal behaviour within a specific context. Tasks such as summarization of a text are performed by reinforcement learning algorithms.
  • Company monitoring- The impact of social media is irreplaceable. It has become an integral part of the normal life of every individual and perhaps this is the reason why companies and organisations have started focusing on social media interactions for promotions and growth of their business and reach more than ever before And social media monitoring tools such as Buffer and Hootsuite have been built using the latest algorithms of NLP. Tools like these help in monitoring company’s engagement in the market.


General Natural Language Processing Tasks

Now as we have seen various applications of NLP, let us walk through the general NLP tasks that are followed when NLP systems deal with a language-

  • Content categorization– Includes summarization along with content indexing, duplication, and content alerts.
  • Topic discovery and modelling- Deducing meaning from the text and applying analytics
  • Contextual extraction- Extracting information from text-based sources
  • Sentiment analysis-Includes identifying specific moods and opinion mining.
  • Speech-to-text and text-to-speech conversion-Transforming voice commands into written text, and vice versa.
  • Document summarization-Generating structures from large bodies of text.
  • Machine translation- Automatic translation of text or speech from one language to another.

NLP has gained popularity since its inception. Devices like Amazon’s Alexa are being used widely all across the globe today. And for enterprises, business intelligence and customer monitoring are fast becoming popular and will dominate the sector in the coming years.

What is Natural Language Processing?

What is Natural Language Processing (NLP)?

Natural Language Processing commonly abbreviated as NLP is a subfield of computer science and artificial intelligence. It is mainly concerned with the interaction between computers and the languages humans speak, like English, Italian, French, among various others. It is used in particular to program machines to process and analyze large amounts of natural language data.

The development of NLP applications is quite challenging because computers traditionally require human beings to communicate to them through a programming language or a high-level language. Human speech, however, is not always precise, is often ambiguous and is dependent on factors like the emphasis on a particular word or expression. These are the factors that the computer finds very difficult to understand.

How does Natural Language Processing work?

Syntax and Semantic analysis are two main techniques that are used with NLP. The  Syntax is the arrangement of words in a sentence to make some grammatical sense. Different Syntax methods used are:

  • Parsing
  • Word segmentation
  • Sentence breaking
  • Morphological segmentation and
  • Stemming

The Semantic involves the use and meaning behind the words. NLP applies the algorithms to understand the grammar and meaning of the sentences. The techniques used by NLP in semantic Analysis are:

  • Named Entity Recognition
  • Natural Language Generation

The current approaches to NLP are mainly based on Deep Learning, which is a type of AI that examines and uses the patterns in data to improve programs understanding. It is basically dependent on supervised learning, which consists of a training set and a test set.

Three tools very commonly used for NLP are NLTK, Gensim, and Intel NLP Architect. Natural Language Toolkit(NLTK), is an open-source python module with data sets and tutorials. Gensim is a Python library for topic modeling and document indexing. Intel NLP Architect is also another Python library for deep learning topologies and techniques.

What are the Uses of Natural Language Processing?

Although, NLP came into existence for the first time by Alan Turing when he published an article titled “Computer Machinery and Intelligence,”. The vast use came into effect only from the 80s, after the introduction of Machine Learning. Before 1980, the most natural language processing systems were based on complex sets of hand-written rules. Writing these rules included a lot of labor and was inaccurate due to diversity in the pronunciation of a language. Introduction of Machine Learning speeded up the development of Natural Language Processing.

Natural language Processing is very widely used today in our daily routine. It finds its application in:

  • Chatbots – Chatbots handle various clients and answer their query without considerable human effort. Chatbots are trained on a vast set of data and hence process only the essential part from a conversation. Companies like Uber, Zomato use the Chatbots to minimize human involvement.
  • Voice Assistants – The most significant use of NLP is implemented for this purpose. Technological giants like Google, Microsoft, Amazon, etc. use their own personal voice assistant to help in communicating with smart devices quickly. Amazon assigns over 1000 personnel globally for enhancing its voice assistant.
  • Very brilliant use of NLP came with the name Grammarly, which is a tool that keeps on a check on writers’ write-ups, and points out grammatical errors and suggests better phrases.
  • Google translate also uses NLP to translate a webpage from one language to another by understanding its content.


What are the Challenges faced by NLP?

NLP, though a new technology with a lot of advantages isn’t completely developed. For Example – Semantic and Grammar Analysis is still a challenge for NLP. Other difficulties includes, NLP not relating to sarcasm easily, since NLP cannot figure the changing meaning of words on the basis of speaker emphasis. NLP is also challenged by the fact that the dialect of people changes with regions.

On a final note, Natural Language Processing is a very handy tool, although it is in the developing state and faces some difficulties. The recent development in the NLP has made it a gem for the Technological Giants. The future of NLP through Machine and Deep Learning seems quite bright.