Introduction to NLP
The following article provides an outline for Introduction to NLP. NLP (Natural Language Processing) is going to be the biggest leap of humankind in the near future and in the field of AI so far. Readers, do not confuse yourself with any resemblance to the Hollywood movie of Will Smith – iRobot. No resemblance to it in any way.
Let’s see what exactly NLP is and why there is so much hype associated with it.
You must have heard these names somewhere Google Assistant, Siri, Alexa and Cortana. Now it’s time to add one more addition to this list; yes, we are talking of GOOGLE DUPLEX. With saying that, this blog is focused completely on introduction to NLP and not on Google Duplex but for our readers, we have provided a kind of latest and most relatable practical example of NLP. GOOGLE DUPLEX is the future of GOOGLE ASSISTANT.
What is NLP?
The definition is very simple if you understand the 3 words, i.e. Natural Language Processing. NLP involves machines or robots to understand human language, the way we humans talk so that they can effectively communicate with us. It means the processing of human language automatically.
Classifications of NLP
Given below is the classifications of NLP:
NLP is classified into two areas:
- Natural language understanding
- Natural language generation
Phonology refers to the science of understanding sound, Morphology refers to word formation, and syntax refers to the structure, whereas Pragmatics refers to understanding.
Components of NLP
Here there are two things that we have discussed in the classification section. For any communication to take place, these two things are necessary. The first is understanding, and the other is a generation (as known as responding in a more common language).
When humans talk to each other, the first thing that other humans do is understand the context. Later formulate the response accordingly that makes sense. This is what the two terms try to say with Natural language understanding, it means to understand the context, and Natural language generation relates to sensible response to the context.
1. Natural Language Understanding
If you know what is ambiguity (different meaning of any particular thing), this term directly relates to this word.
- Lexical (Word Level): Lexical work at the word level; imagine any word used as a verb and used as a noun. These are crucial to deciding for NLP.
- Syntactical (Parsing): Parsing is a kind of synonym for syntactical as per NLP is concerned. Eg. “Call me a cab” this sentence has two implications if you think. One is a request to get a cab while the other implementation says; my name is cab so call me a cab. This is syntactical, which lays its role at a sentence level.
- Referential: Let see a new scenario to understand this better. “Alex went to Dave; he said that he was hungry”. This is just an explanation statement to demonstrate how complex the interpretations can be for the computers to understand in their initial NLP phase. So, in the above statement, the confusion for a computer to understand two he’s is meant for which person (means Alex or Dave).
2. Natural Language Generation
So the machine has understood that we asked them to do something, now come to their turn to provide a proper response or feedback. NLG does the same thing.
- Text Planning: This means plain text from the knowledge base, just like we humans have a vocabulary that helps us to frame sentences.
- Sentence Making: To arrange all the words and make an arrangement in a meaningful pattern.
- Text Realization: To process all the sentences in a proper sequence or order and give the output is called text realization.
Until 1940 this term has no existence, but the first term came was ‘Machine Translation (MT)’. Russian and English were prominent languages working after this technology. Late in the 1960s, some influential work regarding AI has begun, and LUNAR and WINOGRAD SHRDLU were carried in their names.
Applications of NLP
NLP has a wide spectrum of applicability. Only a tip of the iceberg features has been explored, and the rest is still in progress. So far, areas like Machine Translation, Email spam detection, Information Extraction, Summarization, and question answering are some of the explored and worked areas.
- Machine Translation is very crucial as the entire world is present online and the task of data accessible to each individual is a huge challenge. Language barrier contributes most to the challenge, with every language associated is a multitude of structure and grammar.
- Spam filtering works using text categorization, and in recent times, various machine learning techniques have been applied to text categorization or anti-spam filtering, just like Rule learning, Naïve Bayes models.
- Information extraction concern with identifying more relevant and correct textual data. Many applications for whom extracting entities such as names, places, dates, and time are a powerful way of summarizing the relevant information as per the user’s need is concerned.
- Summarization, As we are currently surrounded by data, which means our ability to understand it. Since data is on an ever-increasing trend and the ability to summarize it with exact meaning is high in demand. This gives us a better chance to manipulate data and also to take necessary decisions (which is what NLP is trying to do).
Though the entire introduction to the NLP article revolves around and talks about some of the other ways NLP can make our life easier.
- Automatic summarization with a click readable summary.
- Co-reference resolution.
- Discourse analysis.
- Better result.
- Search processing translation.
- More data extraction and more data growth.
- Complex search results.
Technologies using NLP
- Mental illness analysis.
- Electronic health monitoring.
- NLP algorithms.
- NLP site search.
This has been a guide to Introduction to NLP. Here we discussed the classification, components, and advantages of NLP. You can also go through our other suggested articles to learn more –