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.

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.