Natural language processing( NLP ) applications have seen a boom in the past few years. The main aim of NLP is to decipher and understand human language and make sense of it. NLP has been used to summarize texts, create chatbots, do sentiment analysis, and auto-tagging. The new feature introduced by twitter to tag the tweets with a fact check is part of an NLP feature, topic extraction, but they haven’t released it for everyone. As human-generated data has exponentially increased on many platforms, it has become impossible for the businesses to check or interact with that data manually, here the applications of NLP take charge.

For example, if a business wants to check the reviews of their new product and make inferences from it, allotting people for reading the reviews and categorizing them will be inefficient, as there are lakhs of reviews. But NLP can help here; by doing sentiment analysis of all the reviews and classifying them into negative, positive, or neutral. The tags can be more personalized by changing the algorithms.

NLP has also helped out with the customer care services of a business, replying to a lot of queries that need lots of people. As the internet reach is increasing more and more questions arise, to automate this process and make it more efficient, businesses use chatbots. These chatbots can be customized for a specific market like Kotak Mahindra Bank now uses Keya as its virtual assistant.

How NLP works?

Making sense of the syntax is one of the most critical things for applying NLP algorithms; some of the syntax analysis methods are:

  • Lemmatization: It means reducing the various inflected forms of a word into a single form for a more straightforward analysis.
  • Morphological segmentation: It involves dividing words into individual units called morphemes.
  • Word segmentation: It consists of dividing a large piece of continuous text into distinct units.
  • Part-of-speech tagging: It involves identifying the part of speech for every word.
  • Parsing: It consists in undertaking a grammatical analysis for the provided sentence.
  • Sentence breaking: It consists in placing sentence boundaries on a large piece of text.
  • Stemming: It involves cutting the inflected words to their root form.
  • Morphological segmentation: It consists of dividing words into individual units called morphemes.
  • Word segmentation: It consists of dividing a large piece of continuous text into distinct units.
  • Part-of-speech tagging: It involves identifying the part of speech for every word.
  • Parsing: It involves undertaking a grammatical analysis for the provided sentence.
  • Sentence breaking: It involves placing sentence boundaries on a large piece of text.
  • Stemming: It involves cutting the inflected words to their root form.

Open-source libraries for NLP

There are many NLP libraries on the internet, some of the most popular libraries are:

  • NLTK: It is the most featured library; the most used combination is nltk(Natural Language Toolkit) and python. This library focuses on research and education in the field of NLP. There are numerous tutorials for nltk on the internet. Nltk provides plenty of components for NLP tasks, including tokenization, tagging, stemming, parsing, and classification. It has an active community. This library is a bit slow as compared to others. It is suitable for exploration and experimentation in NLP.
  • SpaCy: This is a fast, easy to use, well-documented library. It can handle large volumes of data and has pre-trained NLP algorithms that make your tasks easier. This library is for production usage, written in the programming language Cython; it provides the fastest syntactic parser in the market. Although the menu of this library is limited, there are fewer choices.
  • TextBlob: This library is an extension of nltk. It brings out the functionality of nltk in a more straightforward manner. It is very beginner-friendly, best for sentiment analysis, text classification, and parts of speech tagging. This library is suitable for smaller projects.

Saas Tools for NLP

People want to generate insights from texts, but don’t want to delve deeper into the working, some of the tools for non-technical people are:

  • Amazon Comprehend: Amazon Comprehend is a natural language processing software that uses machine learning to look for insights and relationships within a text. AutoML features the software that allows customers to build a custom set of entities and text models as per the organization’s needs.
  • Aylien: Aylien text API is a package of Natural Language Processing, information retrieval, and Machine Learning tools that allows developers to extract meaning and insights from documents with ease.

Use cases of NLP

Finance companies now check a client’s creditworthiness by analyzing thousands of data points related to the customer and create a credit score even if a person has never used a credit card. A user leaves many digital footprints while surfing the internet that helps in predicting the user’s behavior. Many companies use NLP algorithms to filter out resumes and to make the process bias-proof. Advertising companies analyze the digital footprints of a user to predict their potential audience interested in their products.NLP software helps increase the range of channels for ad placement.

Healthcare is entering a new era with NLP applications, data mining integration in healthcare systems helps doctors make more well-informed decisions and improve diagnosis, and patient treatment.

Chatbots are a prime example of automation tech. They can have personalized conversations with customers, quickly solve the necessary queries, and bring in a human to answer advanced questions.

The stock markets are news sensitive and react to even the slightest change in the market; many financial companies are integrating historical data, news archives, company reports, and other relevant data in their ML models to make accurate predictions.

A person can track his/her reputation on the internet and can predict his/her brand value easily. Companies monitor their competitors using NLP, how their competition is performing, the comparison parameters, etc.

The spam filter is one of the most common use cases, Emails which are spam are filtered, and the useful emails show up on the feed.

Voice-activated assistants are gaining a lot of traction, google assistant, Siri, and Alexa all these assistants are becoming smarter daily and making life more comfortable. Some time ago, these assistants were not as accurate as they are now; they can now accurately predict what their users are saying and follow their instructions.

Recent advances in NLP

NLP is hard; the grammar makes it more complicated. There can be many meanings of a single sentence, and one must understand the context of the conversation and move forward. Humans do it with ease; taking over the world seems like a dream for robots when they cannot even understand human language. But recent advancements have made it more accessible.

Attention is a very influential idea in Deep learning. Before this idea, neural machine translations were based on encoder-decoder RNNs/LSTMs.The encoder LSTM processes the entire input sentence and encodes it into a context vector. The decoder LSTM produces the words in a sentence one after another. The drawback of this approach was that the whole process was dependent on the summary provided by the encoder, and the encoder performed very poorly in the case of long sentences.

The idea behind attention is — while performing a specific task, for example, translation of a sentence from one language to another, say English to Spanish. Each word in the Spanish sentence formed would be suggested of all terms in the original input with varying degrees of attention or importance rather than generating a single context by processing the whole English sentence. It would make the translation more accurate.

Most of the businesses are using NLP to increase their customer engagement, lessen the ambiguity, analyzing reviews, and gaining an edge against their competitors. The businesses which are not using NLP are getting into that field and fast-tracking their entry, investing some money to increase their returns. Companies are trying to take advantage of everything they have available.


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