Few applications of Natural Language Processing include:

 

• Sentiment Analyzer

• Text classification/Clustering

• Email Spam and Malware Filtering

• Chatbots

• Machine Translation

• Question Answering

• Named Entity Recognition (NER)

• Speech Recognition

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1: Sentiment Analyser
Sentiment Analysis is relevant mining of content that recognizes emotional data in the source material. In today's world a sentiment analyser is usally used over different social media sites. This is because there is a plethora of data being posted everyday and analysing this huge data has proven to be very useful in recent years. Deriving emotions behind this large textual data is called sentiment analysis. A sentiment analyser could help businesses to understand how their customers feel about their products and also their company as a whole. With the recent advances in deep learning, the ability of algorithms to analyze text has improved considerably. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research. Sentiment Analysis is the most common text classification tool that analyses an incoming message and tells whether the underlying sentiment is positive, negative or neutral.

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2: Text Classification
Text Classification is a technique in which we assign targets or categories to textual data in accordance with the context of the data. This method is included in the fundamentals of NLP techniques. Sentiment analysis is actually an application of text classification. Other applications of text classification include spam detection, also a faster emergency response system can be made by classifying panic conversation on social media.

Textual data is everywhere, may it be emails, web sites, social media, books or chats. Everywhere the sight goes there is some form of unstructured textual data present. All this data could be made useful only if we know how to extract it and find useful patterns in it. Structuring this large data needs scrutinizing effort but this effort could bring a lot of benefits to an individual or organization.

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Data Collection
This is the first and a necessary step for building any Machine Learning algorithm as all the machine learning algorithms require some data to train on. Data Collection totally depends on the problem at hand, for example in case of sentiment analysis which is an application text classification, needs any raw text with attached target annotations as positive, negative and null. Similarly, depending on the problem we can collect data in any form, may it be reviews of some product of some organization or it could be genre-labeled songs, etc.

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Data Preprocessing
The textual data that we collect is too messy and unprocessed. The messier the data we train our model on the poorer will be the model’s accuracy of prediction. Therefore for this reason preprocessing is one of the most important step of this process. Cleaning textual data is a lot different from cleaning any other data since it contains vocabulary words from any language instead of numbers.

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Feature Selection
Choosing correct features is directly linked with how well an algorithm is going to perform. Different researchers have used different features for different problems. Choosing the right features for a given problem is very important. Some features used for textual classification problem include :

1: Unigram
2: Bigram
3: N-gram
4: POS tagging
5: Subjective features
6: objective features and so on.

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Model Selection
There are many parameters on which choice of model depends for example if the target variable shows some sort of continuity then it is preferred to choose a regression-based model. Similarly, if the target variable consists of categorical values, then, in that case, it would be better not to choose regression. There are different classification algorithms on which these models are built on. For example, the logistic regression algorithm represents a machine learning technique used for classifying a set of data into its given target values. Logistic regression could also be used for regression problems but is widely used for classification problems.

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Model evaluation
One of the most common and appropriate techniques used for the evaluation of a classifier is through a confusion matrix.

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3: Email Spam and Malware Filtering
The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. The person sending the spam messages is referred to as the spammer. Such a person gathers email addresses from different websites, chatrooms, and viruses. When such a spammer starts sending spams it prevents the user from making full and good use of time, storage capacity and network bandwidth. The huge volume of spam emails flowing through the computer networks has destructive effects on the memory space of email servers, communication bandwidth, CPU power and user time. Machine learning methods of recent are being used to successfully detect and filter spam emails.

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4: Chatbots
Chatbots are computer programs that interact with users using natural languages. This technology started in the 1960s; the aim was to see if chatbot systems could fool users that they were real humans. However, chatbot systems are not only built to mimic human conversation and entertain users. Chatbots could be used to meet some specific needs of a business.

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5: Neural Machine Translation
Effort to access other language documents leads to the development of a machine translation system which involves lots of heterogeneous features and its implementations. Information professionals have widely used the advantages of machine translation for satisfying their user's needs. Machine Translation methods are different and each has its own benefits and drawback. No translations tools can generate an exact version of source language but give the gist of information which can utilize to find the type of information contained in the source text. Sometimes, it is necessary to perform post-editing by in-house linguistic after generating translation output with a translation engine.

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6: Question Answering Model
Algorithms of question-answering in a computer system oriented on input and logical processing of text information are presented. A knowledge domain under consideration is the social behavior of a person. A database of the system includes an internal representation of natural language sentences and supplemental information. The answer Yes or No is formed for a general question. A special question containing an interrogative word or group of interrogative words permits to find a subject, object, place, time, cause, purpose and way of action or event. Answer generation is based on identification algorithms of persons, organizations, machines, things, places, and times.

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7: Named Entity Recognition-NER
Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognize named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results.

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8: Speech Recognition system
The speech recognition system at its core translates the spoken utterances to text. There are various real-life examples of speech recognition systems. For example, Amazons Alexa, which takes the speech as input and translates it into text. The advantage of using a speech recognition system is that it overcomes the barrier of literacy.

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