What is Natural Language Processing?
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language.
It uses computational linguistics (ruled-based modeling of natural language) with machine learning, statistical, and deep learning models to analyze natural language and understand the actual meaning of text or voice data. It even understand’s the speaker’s or writer’s sentiment and intent.
It isn’t a new science, but has been advancing at a fast pace due to the availability of big data, increasingly effective algorithms, and the heightened interest in human-to-machine interaction and communication.
Why is Natural Language Processing important?
The main reason why Natural Language Processing is extremely important is that it helps analyze and make sense of vast volumes of data. It helps process text as well as voice data, understands sentiments and intents and even helps derive critical insights from the data.
Natural language is extremely complex and the data is largely unstructured. Textual data contains misspellings, abbreviations, missing punctuations, while voice-based data has the issue of regional accents, mumbling, stuttering, etc. When speaking or writing, different languages have different grammar and syntax rules. We even end up using words and phrases from other languages quite often.
Natural language processing (NLP) is of critical importance because it helps structure this unstructured data and reduce the ambiguity in natural language.
It even helps businesses organize their data in a manner that works more effectively for them.
How does Natural Language Processing work?
Before Natural Language Processing systems can actually analyze the data and understand human language, there are some pre-processing tasks that need to be done.
- Tokenization: This involves segmenting text into single clauses or tinier semantic units.
- Part-of-speech-tagging: This process is about tagging words as nouns, verbs, adjectives, adverbs, pronouns, etc.
- Stemming & lemmatization: These involving reducing words to their root form
- Stop word removal: Getting rid of words like articles and prepositions that carry little or no additional information.
After that, the NLP system uses an algorithm to interpret natural language and carry out tasks.
The two most commonly used types algorithms are:
1. Rule-based algorithms
Systems that use these algorithms are reliant on grammatical rules that are created by expert linguists.
2. Machine learning algorithms
Machine learning algorithms don’t need manually defined rules. They are fed training data and learn from it.
What are the applications of Natural Language Processing?
Here are a few tasks for which NLP is employed:
Chatbots are used to automate customer support, lead generation, sales, and other functions. They use NLP to understand a user’s query and deliver an appropriate response.
2. Machine translation
This involves automating the translation of data from one language to another. Translation models can even be trained for particular domains to increase the accuracy of the translation.
3. Sentiment analysis
This is used to analyze text and detect nuances in emotions and opinions. It helps you understand how positive or negative the sentiment of the data is.
Sentiment analysis is used for market research, social research, analyzing customer feedback, understanding the Voice of the Employee, etc.
4. Text summarization
This involves generating synopses of large volumes of text by extracting the most critical and relevant information.
5. Speech recognition
This refers to using NLP to turn voice data into a machine-readable format.
6. Intent classification
NLP can be used to understand the underlying motivation and the purpose behind text data. It is very useful in customer support, marketing, sales, etc.