Hi folks, Here in this article we are going to see basic terminology and learn about Natural Language processing.
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We are surrounded by millions or trillion of data and counts increasing day by day, So some genius think that, what if computer itself recognise and take decision what human say and what they mean and hence they began this field – Natural language processing in 1940’s, after world war II.
At this time, people recognised the importance of translation from one language to another and hoped to create a machine that could do this sort of translation automatically. Later on (after seeing its importance and relevance – actually uses of NLP) they start researching and adding logics and give computer some brain in terms of algorithms with maths to work more accurately.
there are several application are there, which is used by millions of people in the world and growing rapidly. some are listed below for lots of Motivation
- Email Filters
- Language Translation
- Search Results
- Text Analytics
- Information retrieval
- Sentiment analysis
and many more, no limitation of its uses, every problem has their own solution ( as we are talking here NLP). NLP might use to tag some specific words to categories their importance.
Image source – Internet
Natural language processing
The field of study that focuses on the interaction between human language and computer is called Natural language processing.
Natural Language Processing is a field that covers computer understanding and manipulation of human language, and it’s ripe with possibilities for news gathering,” Anthony Pesce says in Natural Language Processing in the kitchen. “You usually hear about it in the context of analysing large pools of legislation or other document sets, attempting to discover patterns or root out corruption.”
So the question is – What actually define Natural language Processing, We the application , when started, why started – So here with Why, We are going to see actual meaning of NLP .
What is natural language processing ?
Natural language processing (NLP) is a field of artificial intelligence in which computers analyse, understand, and derive meaning from human language in a smart and useful way. with the help of NLP, developers can organise and structure knowledge to perform tasks such as : automatic summarisation, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
NLP is used to analyse text, row unstructured text – allowing machine to understand how human speak. This Human – Computer interaction enables real-world applications. NLP is commonly used for text mining, machine translation, and automated question answering.
NLP considered as difficult problem in computer science. Human languages rarely precise or plainly spoken, So to understand human language is to understand the not only words but the concept how they are linked and connect to form meaning of that. Ambiguity of human language (not precise and accurate every time ) make natural language processing is more difficult.
NLP – based on Machine learning
NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analysing a set of examples (i.e. a large corpus, like a book), and making a statistical inference. In general, the more data analysed, the more accurate the model will be.
Human language is complex by nature. A technology must grasp not just grammatical rules, meaning, and context, but also colloquialisms, slang, and acronyms used in a language to interpret human speech. Natural language processing algorithms aid computers by emulating human language comprehension.
Lemmatizations and Stemming
- Term Frequency
Named Entity Recognition
- Named Entity Identification
- Named Entity Classification
Bag of Words
Open source NLP library
This library provide the algorithmic building blocks of NLP in real-world applications.
Natural Language Toolkit (NLTK): A Python library that provides modules for processing text, classifying, tokenizing, stemming, tagging, parsing, and more.
NLP can seem like an abstract concept. Still, as we’ve seen in many NLP examples, it is a very useful technology that can significantly improve business processes – from customer service to eCommerce search results.
In Next article we will see the real-world applications – With case studies and solution with code. Thanks 🙂