Updated March 18, 2023
What is Natural Language Processing?
Natural Language Processing is the technique used by computers to understand and take actions based upon human languages such as English. It is a part of Artificial Intelligence and cognitive computing. The process involves speech to text conversion, training the machine for intelligent decision making or actions. Natural Language Processing or NLP works on the unstructured form of data, and it depends upon several factors such as regional languages, accent, grammar, tone, and sentiments. There are certain steps that NLP uses, such as lexical analysis, syntactical analysis, semantic analysis, discourse integration, and pragmatic analysis. Some of the popular NLP implementations are Amazon Alexa, Google Assistant, and Chatbots.
Understanding Natural Language Processing
Artificial intelligence means making computers as intelligent as a human. Natural language processing is a component of AI. Natural language processing enables computers to understand, perform an action and interact with Humans using their language. It can be used in many areas like passing commands to perform some action, converting speech to text, documenting it, telling directions in automobiles, etc.
But NLP (Natural Language Processing) is not easy to implement. Computers are designed to work with structured data, follow well-defined commands and use standardized language. They are very systematic in terms of their processing. But natural language is not structured. There are many factors that may influence the language spoken by a person, like a region, locality, slang, pronunciation, etc. Even the same word can have different meanings depending upon the context. Hence, to make a computer smart enough to understand and work with a human in their language, it needs to be designed to understand the flexibility of Natural Language. It should be able to decipher what exactly a person wants to say in a given context.
With the power of machine learning, computers can be taught natural language. Multiple sets of text will be fed to computers and process the sets using text analyzer algorithms to teach the computer about how natural language works.
Techniques used in Natural Language Processing
Natural language processing helps in extracting valuable information from a text and learn from it.
Consider the below paragraph as an example.
Company xtz airline provides a decent service. All the staff members are very co-operative, especially flights attendant Nora, James, and Liya. The only problem with the flights is that they got delayed very often. Even sometimes, flights get canceled also.
Below are some of the techniques used in NLP:
- Grammar Induction: It helps in writing correct grammar. The above example word ‘gets’ is not used correctly and will get highlighted when used in the NLP engine.
- Sentiment Analysis: NLP is used to analyze the positive and negative nature of the sentence. For example, in the above paragraph, ‘airline provides decent service’, and ‘staff members are very co-operative’ are positive comments, whereas ‘got delayed very often’ is a negative comment.
- Aspect Mining: NLP uses aspect mining to determine which aspect is positive and which aspect is negative. In the above example, the staff is a positive aspect whereas flight service is a negative aspect.
- Name Entity Recognition: It is used to recognize the names of important persons, organizations, locality, dates, etc. For example, XYZ airlines, Nora, James, and Liya.
- Summarization: NLP can also be used to summarize text and provide a crux of the text. It gives ranks to sentences according to their similarity with other sentences. Thus, the sentence with the highest rank is included in the summary.
- Topic Recognition: NLP analyzes the text and finds the topic to which the text is mainly related. It will extract some keywords and categorize them under some topic. For example, in the above text, the two main topics are “Staff Member” and “Flights”.
- Speech to Text Conversion: If the above paragraph had been a response to a feedback call, then it can be recorded and converted into text. After this, it can be further analyzed for service improvements.
Working of Natural Language Processing
Although a part of AI, NLP uses machine learning techniques to extract information and learn from that. Machine learning algorithm works on the fundamental of learning while performing. NLP also keeps on learning based on input given. That’s why despite mistakes and common language styles, NLP tends to predict correctly what the user wants to say.
Below are the steps of NLP:
- Lexical Analysis: Lexical analysis means dividing the whole text segment into sentences and analyzing their meaning.
- Syntactical Analysis: This method involves the association of words with other words, their arrangement in the sentence, and their relative meaning.
- Semantic Analysis: In this analysis, the meaningfulness of the sentence is checked.
- Discourse Integration: This method analyses the relative meaning of sentences and their association with other sentences.
- Pragmatic Analysis: This method deals with the real-world meaning of the sentence.
Importance of Natural Language Processing
The amount of data available online is increasing day by day. Most of this is unstructured text. To get valuable information out of this data is a challenging task. NLP can be used in this case.
NLP techniques can be used for speech-to-text conversion, for those who can not type can use NLP to document things. NLP analysis can be used to analyze sentiment and thus helps businesses in gaining customer satisfaction. IT helps users who are unfamiliar with technology work with it easily.
Uses of Natural Language Processing
Below are some of the usage of NLP:
- Sentiment analyzer
- Speech to text
- Voice recognition
- Text classification
- Document summarization
- Question answer
- Spell checking
- Grammar checking
Examples of NLP
Over the past few years, technology trends such as Artificial intelligence have become popular. As a result, efforts have been put to make a computer as smart as a human. This has gained a success to some extent. NLP is part of such efforts.
Below are some of the examples where NLP is used:
- Amazon Alexa (Machine Translation)
- Google Assistant (Voice Recognition)
- Grammarly (Check grammatical error)
- Chatbot (Question/ answer)
- Search Autocomplete
- Spell check (Spelling check)
- Customer Service chatbot
- Robots performing actions on command
- Automobiles assistant
Conclusion
NLP plays a vital role in machine-human interaction. In the future, we can see more and more development in this field. It can make day-to-day life easier and smarter.
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