Applications of NLP

Machine Translation

Machine translation (MT), process of translating one source language or text into another language, is one of the most important applications of NLP. We can understand the process of machine translation with the help of the following flowchart −

Types of Machine Translation Systems

There are different types of machine translation systems. Let us see what the different types are.

Bilingual MT System

Bilingual MT systems produce translations between two particular languages.

Multilingual MT System

Multilingual MT systems produce translations between any pair of languages. They may be either uni-directional or bi-directional in nature.

Approaches to Machine Translation (MT)

Let us now learn about the important approaches to Machine Translation. The approaches to MT are as follows −

Direct MT Approach

It is less popular but the oldest approach of MT. The systems that use this approach are capable of translating SL (source language) directly to TL (target language). Such systems are bi-lingual and uni-directional in nature.

Interlingua Approach

The systems that use Interlingua approach translate SL to an intermediate language called Interlingua (IL) and then translate IL to TL. The Interlingua approach can be understood with the help of the following MT pyramid −

Transfer Approach

Three stages are involved with this approach.

In the first stage, source language (SL) texts are converted to abstract SL-oriented representations.
In the second stage, SL-oriented representations are converted into equivalent target language (TL)-oriented representations.
In the third stage, the final text is generated.

Empirical MT Approach

This is an emerging approach for MT. Basically, it uses large amount of raw data in the form of parallel corpora. The raw data consists of the text and their translations. Analogybased, example-based, memory-based machine translation techniques use empirical MTapproach.

Fighting Spam

One of the most common problems these days is unwanted emails. This makes Spam filters all the more important because it is the first line of defense against this problem.

Spam filtering system can be developed by using NLP functionality by considering the major false-positive and false-negative issues.

Existing NLP models for spam filtering

Followings are some existing NLP models for spam filtering −

N-gram Modeling

An N-Gram model is an N-character slice of a longer string. In this model, N-grams of several different lengths are used simultaneously in processing and detecting spam emails.

Word Stemming

Spammers, generators of spam emails, usually change one or more characters of attacking words in their spams so that they can breach content-based spam filters. That is why we can say that content-based filters are not useful if they cannot understand the meaning of the words or phrases in the email. In order to eliminate such issues in spam filtering, a rule-based word stemming technique, that can match words which look alike and sound alike, is developed.

Bayesian Classification

This has now become a widely-used technology for spam filtering. The incidence of the words in an email is measured against its typical occurrence in a database of unsolicited (spam) and legitimate (ham) email messages in a statistical technique.

Automatic Summarization

In this digital era, the most valuable thing is data, or you can say information. However, do we really get useful as well as the required amount of information? The answer is ‘NO’ because the information is overloaded and our access to knowledge and information far exceeds our capacity to understand it. We are in a serious need of automatic text summarization and information because the flood of information over internet is not going to stop.

Text summarization may be defined as the technique to create short, accurate summary of longer text documents. Automatic text summarization will help us with relevant information in less time. Natural language processing (NLP) plays an important role in developing an automatic text summarization.

Question-answering

Another main application of natural language processing (NLP) is question-answering. Search engines put the information of the world at our fingertips, but they are still lacking when it comes to answer the questions posted by human beings in their natural language. We have big tech companies like Google are also working in this direction.

Question-answering is a Computer Science discipline within the fields of AI and NLP. It focuses on building systems that automatically answer questions posted by human beings in their natural language. A computer system that understands the natural language has the capability of a program system to translate the sentences written by humans into an internal representation so that the valid answers can be generated by the system. The exact answers can be generated by doing syntax and semantic analysis of the questions. Lexical gap, ambiguity and multilingualism are some of the challenges for NLP in building good question answering system.

Sentiment Analysis

Another important application of natural language processing (NLP) is sentiment analysis. As the name suggests, sentiment analysis is used to identify the sentiments among several posts. It is also used to identify the sentiment where the emotions are not expressed explicitly. Companies are using sentiment analysis, an application of natural language processing (NLP) to identify the opinion and sentiment of their customers online. It will help companies to understand what their customers think about the products and services. Companies can judge their overall reputation from customer posts with the help of sentiment analysis. In this way, we can say that beyond determining simple polarity, sentiment analysis understands sentiments in context to help us better understand what is behind the expressed opinion.

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