NATURAL LANGUAGE PROCESSING

 

                  Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. It’s at the core of tools we use every day – from translation software, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in a number of fields, including medical research, search engines and business intelligence.

 

 

How Does Natural Language Processing Work?

               NLP enables computers to understand natural language as humans do. Whether the language is spoken or written, natural language processing uses artificial intelligence to take real-world input, process it, and make sense of it in a way a computer can understand.Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagramed sentences in grade school, you’ve done these tasks manually before.

                 Using text vectorization, NLP tools transform text into something a machine can understand, then machine learning algorithms are fed training data and expected outputs (tags) to train machines to make associations between a particular input and its corresponding output. Machines then use statistical analysis methods to build their own “knowledge bank” and discern which features best represent the texts, before making predictions for unseen data.

 There are two main phases to natural language processing: data preprocessing and algorithm development.Data preprocessing involves preparing and "cleaning" text data for machines to be able to analyze it. preprocessing puts data in workable form and highlights features in the text that an algorithm can work with. There are several ways this can be done, including:

• Tokenization. This is when text is broken down into smaller units to work with.
• Stop word removal. This is when common words are removed from text so unique words that offer the most information about the text remain.
• Lemmatization and stemming. This is when words are reduced to their root forms to process.
• Part-of-speech tagging. This is when words are marked based on the part-of speech they are -- such as nouns, verbs and adjectives. 

 

 

Benefits of natural language processing

    The main benefit of NLP is that it improves the way humans and computers communicate with each other. The most direct way to manipulate a computer is through code -- the computer's language. By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans.

 Other benefits include:

1. Perform large-scale analysis
2. Get a more objective and accurate analysis
3. improved accuracy and efficiency of documentation;
4. Improve customer satisfaction
5. Better understand your market
6. provides advanced insights from analytics that were previously unreachable due to data volume.
7. Gain real, actionable insights
   

 

Natural Language Processing Examples

 

Online search engines

Whenever you do a simple Google search, you’re using NLP machine learning. They use highly trained algorithms that, not only search for related words, but for the intent of the searcher. Results often change on a daily basis, following trending queries and morphing right along with human language. They even learn to suggest topics and subjects related to your query that you may not have even realized you were interested in.

Email Filters

A spam filter is probably the most well known and established application of email filters. Spam makes up an estimated 85% of total global email traffic worldwide, so these filters are essential. But filters have also evolved as a way to help people keep their inbox organized. For instance, in gmail your emails can be sorted into primary, social, promotions, and updates.

 

Predictive text

Every time you type a text on your smartphone, you see NLP in action. You often only have to type a few letters of a word, and the texting app will suggest the correct one for you. And the more you text, the more accurate it becomes, often recognizing commonly used words and names faster than you can type them.

 

Language Translation

Online translators are now powerful tools thanks to Natural Language Processing. But that wasn’t always the case. If you think back to the early days of google translate, for example, you’ll remember it was only fit for word-to-word translations. It couldn’t be trusted to translate whole sentences, let alone texts. Now, however, it can translate grammatically complex sentences without any problems. This is largely thanks to NLP mixed with ‘deep learning’ capability. Deep learning is a subfield of machine learning, which helps to decipher the user's intent, words and sentences.