NLP and its Various Applications
By: Aditya Pimpley
Tags: Data Science, NLP, Naturallanguageprocessing

NLP or Natural Language Processing is a hot, talked about topic in the Data Science Sector today. More and more people are trying to get into this field, companies are putting a lot of money into research.

Lets understand why

What is NLP?

NLP is a method to make machines understand and perceive information from text in a way a human would. Machine learning works on the principle of making machines think and operate like a brain could and as it is very much capable of understanding text and speech, we tend to provide the same power to the machine so that it can automatically understand a text or speech in its natural form. The main motive of NLP is to read, understand and decode human words in a valuable manner.

A simple example of exchange between humans and machines using NLP could be as follows

1)A human speaks and the computer captures the audio

2)It then tries to turn the audio to text and process it

3)The computer translates the text and converts it to audio

4)It plays the translated audio back to the human

How NLP works

In Natural Language Processing it is necessary to apply algorithms to recognize and bring out the rules of natural language so that the raw language data available could be transformed into a form which is machine understandable. When we feed text to a computer, it uses algorithms to recognize the meaning related to every sentence and gather the essential data from them. However the real meaning may sometimes not get extracted and the sentence might get translated literally thus failing to convey its true meaning. Extracting true meaning out of text is a tough task and to simplify it, we make use of pipelining.

Syntactic and semantic analysis techniques are used to decode text and understand it.

1)Syntax

In Natural Language Processing, syntactic analysis is used to interpret the way a natural language behaves with the rules of grammar. A fixed set of algorithms are used to apply the grammar rules to words and extract their meaning.

Syntax further includes some specific techniques:

Lemmatization: The process of reducing the multiple forms of a word into a single form for identification and a hassle-free analysis

Morphological segmentation: The process of dividing the of words into single units called as morphemes.

Word segmentation: Separating the unique words in a sentence

Part-of-speech tagging: Finding the appropriate part of speech for each word

Parsing: Grammatical analytics for given sentence

Sentence breaking: Separating individual sentences from a massive piece of text

Stemming: Includes cutting the inflected words mainly their suffix to get the original form

2)Semantic Analysis

Semantics involves making use of computer algorithms to find out the interpretation of words and structures of the sentences.

Here are some techniques in semantic analysis:

Named entity recognition (NER): It finds the parts of a text that identify and classify into predetermined groups. Some common examples would be names of places and people.

Word sense disambiguation: It determines the sense of a word based on some given context.

Natural language generation: It makes use of the database to get semantic intentions and convert it into human language.

Now let’s move on to some applications of NLP

Applications of NLP

Spell Checker

A spell checker is a software which identifies and corrects spelling errors or typos in a text. A popular example of spell checker software is Grammarly. It’s a program that integrates with a variety of text documents, automatically checking for any spelling errors as the user continues to write, and suggests corrections accordingly.

A Spell Checker will first scan and extract words from the document. It then searches the word string in its dictionary of known strings. Any string which is not found in the dictionary is marked as a misspelling. The known string nearest to the misspelled word is shown as the suggested correction. One technique is to use set similarity where a similarity coefficient like the Jaccard Coefficient is used to find the most similar word. Other techniques include using the Levenshtein distance which is the minimum number of edits needed to transform one string into the other using n-grams which also check the context to suggest the correct use.

And while Spell Checkers have a lot of importance in software applications that create and format word documents, they are also an integral part of websites’ search function where there are high chances of users making spelling mistakes, which may affect the search results.

Chatbots

Chatbots are used for automatic question answering, to understand humanlanguage and give a proper reply through natural language generation.

Regular question answering systems follow set rules, while AI-powered chatbots and virtual assistants are able to learn from every interaction and understand how they should respond. The best part is that they learn from interactions and improve with time. NLU(Natural Language Understanding) is used to understand the meaning of user inputs which makes use of semantic and syntactic analysis as discussed earlier in the article and NLG(Natural Language Generation) to produce understandable text in human language.

These intelligent machines are increasingly present at the frontline of customer support, as they can help teams solve a majority of all routine queries and only route the more complex issues to human agents. The availability is 24/7.

Text Prediction

Text prediction is the process of estimating the next word in a phrase or a sentence. The most popular and common example of text prediction is Google Search.

Google uses a model known as BERT (Bidirectional Encoder Representations from Transformers),it is a Natural Language Processing (NLP) algorithm that makes use of neural networks to create pre-trained models.

These models are then trained using a large amount of unannotated text which is available on the web. The BERT algorithm helps the search engine understand queries in a way which is similar to humans. Other than this, many other applications like Google Docs, Gmail Compose use NLP language models that help them in text prediction.

Machine Translation

Machine Translation is the subfield of computer linguistics which involves using software applications to convert text or speech from one language to the other. During translation, each word in the input sentence is encoded as a number to be translated by the neural network into a resulting sequence of numbers representing the translated target sentence. Each number in the input and output represents a word in the source and target dictionary and are always encoded and decoded accordingly. The best example of machine translation would be Google Translate.

Google Translate uses neural machine translation which uses artificial neural networks (deep learning) to translate the complete sentence at a time. It uses an example-based machine translation approach where the system trains on millions of examples to improve the output.

Automated translation is useful for businesses because it facilitates communication, allows companies to reach broader audiences, and understand foreign documentation in a faster cost-effective way.

Speech Recognition

Natural language processing specifically language modelling plays a crucial role in speech recognition. Any speech recognition model will have 2 parts called the acoustic model and the language model. The acoustic model is incharge of learning features from acoustic content and language models make sure that the predicted sentence semantically and syntactically makes sense. End to end speech recognition is possible with the Bi-LSTM-CTC model which basically takes audio features like MFC and predicts the character sequence.

The advances in NLP have introduced voice as the method of providing inputs to the system, other than clicking, typing, or selecting text. Voice Assistants like Cortana, Siri, Google Assistance, Amazon Alexa are some of the perfect examples of how machines are mastered to recognize the human voice, understand the intent, and respond accordingly. NLP is the heart of Voice User Interfacing today.

Another common example of speech recognition would be the speech to text feature in smartphones. While this is a given feature in many smartphones, there are several mobile apps that make this possible for users. The speech to text feature enables users to provide input through audio which is then converted into text.

Google Voice Search is another example of speech recognition where the users provide voice input to perform the search.

Text Classification

Unstructured text is practically everywhere.Emails, social media, websites, chat conversations being some of the areas where NLP text classification is an essential component and is rigorously used.

One of the most regularly used approaches while categorizing text is bag of words, where a vector represents the occurrence of a word in a predefined dictionary of words. The machine learning model like a SVM is fed with training data that consists of pairs of feature sets (vectors for each text example) and tags (sports, politics, etc.) to produce a classification model.

Text classification algorithms have laid down the basis for software systems that process text data on a large scale. For example, email clients use text classification for flagging emails to certain categories so that they can be sent to the inbox or spam folder.The automatic categorization of emails as Primary, Social, or Promotions in Gmail software are examples of text classification using NLP.

Another region where text classification has a qualitative application is in Discussion Forums. In such areas, text classification algorithms decide if a comment needs to be flagged as inappropriate or not. Along with this, platforms like eCommerce, news agencies, content curators, etc. use text classifiers to automate content/product/service tagging.

Conclusion

NLP and machine learning applications play an important role in bridging the gap between human-machine interactions. With more research in this sector, there are more developments to make machines smarter at learning and understanding the human language.