Hi, today I’m discussing natural language processing or NLP. Now, NLP is a technique of computer science that teaches the computer how to understand the text.
For example, Google uses it to match queries to documents. Alexa, use it to transform our voice into commands. And Facebook uses it to detect spam comments, Speech Recognition, etc.
What is Natural language processing?
One of the most revolutionary things that artificial intelligence can do today is to speak write listen as well as understand human language in simple terms natural language processing is a form of artificial intelligence which attracts meaning from human language to make decisions
based on that information most NLP techniques rely on machine learning to derive meanings from human language, in fact, a simple interaction between humans and machine using NLP can go as follows-
- A human talks to the machine.
- The machine captures the audio.
- Audio to text conversion takes place.
- Processing of the text data.
- Data to audio conversion takes place.
- The machine responds to the human by playing the audio file.
Natural language processing in simple words?
Natural-language is a language that has evolved in humans, through repeating use without any planning. According to statistics, the most popular natural language in the world by their speakers are English with 1.39 billion or 1,390 million speakers, Mandarin Chinese with1.15 billion speakers, Spanish with 661 million speakers, Hindi with 444 million speakers, and Arabic with 422 million speakers.
But we also have artificial languages like Esperanto and Klingon are designed for human communication. Currently, we have more than a thousand different artificial languages in the world.
Now that we understand what is a natural language, let’s start to look at the processing. Generally, there are five steps in natural language processing, which are:
- Stopword removal.
- Part-of-speech tagging.
Tokenization: In most languages like English, we can use the space as the separator and break the sentence into meaningful units called tokens. And we remove punctuations, like comma, colon, full stop along the way as well, and this will give us six tokens.
We can use the same methods for other languages, as long as they use space as the separator between each word, it only becomes more challenging and requires extra work when we are dealing with languages that don’t use space as a separator.
For example Chinese, Japanese, and Thai language.
Stopword removal: After we understand the purpose and how tokenization works, the next step that we need to do is to remove stopwords. Stop words in a language are words that are commonly used but don’t help us much in understanding the content.
For example: ‘the’,’below’, ‘am’, ‘yours’. So, in our example, the stop words here are ‘is’ and ‘a’. If we remove them from the sentence or do evaluate the grammar, it doesn’t affect us much in understanding the meaning of the sentence or at least gathers the context.
Stopword remover is the process to remove words that are frequently appearing in a language and they don’t have the computers to get a context. And also, by doing so, we can reduce the computation time required.
Stemming: Stemming is a process that helps us to normalize word variations back to their root form.
For example, today, if we were to process an article about internet connections, some of the words in the article would be like ‘connects’, ‘connected’, ‘connecting’, ‘connection’, or even ‘connections’.
Without any processing, computers will consider them as six different unique terms, but in fact, they refer to the same word, but they just appear in different tenses and forms. In order to have accurate calculations, all the suffixes should be stem and convert to their basic root form. This is basically how the stemming process works.
A question you might have now is, ‘What about those word variations that do not have a suffix?’ For example, ‘began’,’begun’, ‘drank’, ‘drunk’, ‘flew’ ‘flown’ and ‘rat’,’mouse’ and ‘mice’.
We will need a more advanced technique which is called lemmatization.
Lemmatization: Lemmatization helps us to overcome this problem. We use lemmatization to process words that cannot be normalized by stemming, and we further reduce words of similar concepts to a common root form using a dictionary or any other external knowledge.
The last piece of natural-language processing is part-of-speech tagging.
Part-of-speech tagging: That helps us to further identify the category of the word, to understand its root and relationships so that we can compare it with other terms. It can detect nouns, verbs, determiners, or in a more complex situation, it can also detect other parts of speech like prepositions and pronouns.
What is Natural language processing used for?
Natural language processing is behind the scenes of several things that you take for granted every day when you ask Siri for directions or to send a text. Natural language processing enables that functionality.
let’s take a look at some of the examples of Natural language processing:
Email assistance Autocorrect grammar and spell check as well as autocomplete are all functions enabled by Natural language processing the spam filter on your email system uses NLP to determine what emails you’d like to keep in your inbox and what are likely spam and should be sorted out by answering questions if you have shopped online or interacted with a website chat box you likely were interacting with a chatbot rather than a human these ai customer service gurus are actually algorithms that use natural language processing to be able to understand your query and respond to your questions adequately automatically and in real-time drives e-commerce Natural language processing allows for better search results when you shop online it is becoming adept at deciphering the intent of your message even if there are spelling errors or important details you omit in your search terms by searching online you are actually adding to the
customer data available that help retailers learn your habits and preferences and therefore respond to them.
How does natural language processing work?
NLP entails applying algorithms to identify and extract the natural language rules such that the unstructured language data is converted into a form that computers can understand when the text has been provided the computer will utilize algorithms to extract meaning associated with every sentence and collect the essential data for them.
let’s take an example of a Bengaluru startup Rivera language technologies Rivera’s ai enabled platforms prabandhak and anuvadak combined with neural machine translation and transliteration are great examples of how we use AI(artificial intelligence) for language localization prabandhak is an automated end-to-end translation platform that helps in
translating content in 22 Indian languages it uses a combination of machine translation and manual translators whereas anuvadak as a platform automates repetitive tasks involved in translating hosting and scaling content for localized websites Rivera’s Natural language processing suite enables devices and applications to interact with users like a human in their local language using the NLP suit the device
understands and transcribes speech to text in 11 languages and Indian language the Natural language processing suite is further programmed to suit the Indian context Rivera uses ai enabled language solutions to translate create and manage localization efficiently as industry leaders experiment and continue to develop enhancements in natural language processing such as amazon’s Alexa division using a neural network to transfer learning we can expect that Natural language processing will become better and even more influential for business in the coming years.
Natural Language Processing enables a machine to take what you are saying, make sense out of it, and formulate a statement of its own to respond back to you. As we have seen, it uses a variety of techniques to accomplish this: part-of-speech tagging, tokenization, stemming, lemmatization, named entity recognition, and natural language generation. As a result, NLP allows computers to understand the context and meaning of our words.
Lemmatization & Stemming
Word Sense Disambiguation.
Named Entity Recognition (NER).
Natural language processing is a part/field in machine learning that helps computers to understand human language, manipulate, analyze etc. NLP use in Machine for search queries in google, helps detect spam emails, for predicting the result , auto-correct, speech Recognition.
Natural language processing is used for social media monitoring like mentioning/tag features on instagram and facebook and sending notifications for someone mentioning your brand, sentiment analysis, Text analysis extracting different types of key elements(like dates, locations, people, topics, companies) and creating insights for understanding. Spam filters its uses to filter spam & voileation mails, Auto corrects its uses to correct spelling mistakes.
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