Blog: Data Insights

What is Natural Language Processing and How is it Revolutionizing Business?

October 30, 2018 - by Anish Purohit

You may not realize it, but natural language processing (NLP), a branch of artificial intelligence (AI), is no longer an up-and-coming technology. Rather, it is a popular technology that is being widely used every day by organizations across sectors. Online search, spell-check, spam filters, chatbots — almost all facets of businesses now function with AI-driven NLP algorithms.

What is NLP?

NLP is an AI application that helps computers understand and interpret human language. It uses computer algorithms to identify key elements in human language, then extracts meaning from text or speech, thereby enabling intelligent machines to communicate and process information just like humans.

Although traditionally, machines do not have the ability to understand natural language, NLP can uncover hidden expressions and valuable inference. This helps resolve ambiguity in language and enables quick and efficient information extraction and summarization.

With staggering amounts of unstructured data being generated every day – from customer calls, social media posts, and connected devices – NLP makes it easy to analyze this data efficiently. Since humans express themselves in infinite ways, comprehending the hundreds of different languages, dialects, accents – each having a unique set of grammar, syntax, and slang – is a complex task. Using NLP, computers can interpret natural language, measure sentiment, and identify important parts or elements of speech.

What’s the best part? They can do this without fatigue, and in a consistent and unbiased manner.

How Does it Work?

The main aim of NLP is to analyze and build an association or relationship between phrases. However, this is not an easy task for a machine as it does not understand the emotion and intention behind the language. The use of metaphors and sarcasm in human text make it all the more difficult to interpret the intention behind any conversation. However, using machine learning, you can now train computers to make accurate correlations between different words. By chaining together several machine learning models, NLP breaks sentences into small pieces, and interprets each piece independently.

The 5 Step NLP Process:

1. Lexical analysis involves segmentation of text into words, sentences, and paragraphs. The output is a group of words, phrases, or sentences.
2. Syntactic analysis involves parsing of words and sentences in which grammatically incorrect strings are removed. Such a cleanup helps in building a relationship between the subject and object in a better context.
3. Semantic analysis is an extension of syntactic analysis that helps in checking meaningfulness in text.
4. Disclosure integration involves building the relationship and meaningfulness between two consecutive sentences or phrases.
5. Pragmatic analysis is the most complex of phases that derives interpretation of human language using real-world knowledge.

What are the Applications of NLP?

Considering the benefits of NLP, several organizations across diverse sectors use NLP for a variety of purposes:

1. Customer Service:

As chatbots have become increasingly popular in the customer service industry, NLP can help companies fuel quicker and more relevant conversations through self-service. By intercepting customers’ first service request, NLP can provide answers to some of the most basic questions, allowing humans to focus on more important aspects of their jobs. For example, in the banking sector, an NLP-driven chatbot will be able to guide users on how to open a bank account or shed light on the best account type for a particular customer. For more complex queries, however, the customer will be re-directed to a human agent who will be able to provide a more precise resolution.

2. Spam Identification:

With the ever-increasing problem of unwanted email, spam filters have become a critical first line of defense. And although these filters do a decent job of identifying spam, there are also occasions where unwanted emails still come through, or important emails get accidentally marked as spam. Spam identification is a classic application of NLP that categorizes text into labeled categories using syntactic and semantic analysis on raw and unstructured text.

3. Information Extraction:

NLP solutions are increasingly being used to extract information from unstructured data in the form of text and audio. By making use of co-reference resolution, NLP can analyze the tone, voice, choice of words, and even sentiments, while also finding relational links among words or phrases within sentences. For example, using NLP algorithms, financial companies can extract relevant information from company databases and market trends to drive important trading decisions and achieve profits to the tune of millions of dollars.

4. Summarization:

With such large amounts of data being generated in a digital world, the access to data far exceeds the capacity to understand it. With the information overload trend showing no sign of slowing down, summarizing the meaning of large documents and information is becoming increasingly important. NLP can enable organizations to summarize vast amounts of data, and also understand deeper emotional meanings. For example, by analyzing aggregated data from customer call transcripts or social media, companies can use NLP as a valuable marketing asset to determine the general sentiment for their latest product offering.

5. Intuitive Search:

NLP is also a great asset for enabling intuitive search. By answering specific questions posed by humans, NLP-driven search engines can put a wealth of information at our fingertips. Since every human has the tendency to ask questions in a different manner, they often end up trying a number of different combinations until they find the answer they are looking for. NLP can recognize natural language questions, extract the meaning, and provide the exact answer people are looking for within seconds.

Drive Better Business Efficiency

As businesses look to improve their efficiencies, NLP is making several inroads into the market, including voice-controlled apps in connected cars, healthcare organizations, manufacturing, and retail. According to a report by Statista, the NLP market is expected to be worth $1 billion by 2020. With such a multitude of NLP applications in the market, businesses have to consider the overall organizational readiness for embedding new technologies in existing workflows. With adequate and relevant data for training NLP algorithms, you can drive accurate outputs. Therefore, it is very important to choose applications that can solve business problems with sufficient efficiency and provide value in measurable ways.

Learn more about innovative AI solutions that can transform your business processes.

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