Natural Language Processing has the aim to build machines that interprets and replies to text or voice data. Moreover, reverts back with text or speech automatically, in a way a human does.
What is natural language processing?
Natural Language Processing(NLP) is a branch of computer science, more particularly speaking, a branch of Artificial Intelligence — occupied with giving computers the ability to understand the text and verbal commands given in the same way a human being does.
NLP combines computational linguistics and statistical, machine learning, and deep learning models to model human language. By combining these technologies, computers are able to comprehend human language in the form of text or voice data. And also complete with the speaker or writer’s intent and sentiment.
A computer program can translate text from one language to another, respond to spoken commands, and summarize large amounts of text quickly. In addition to voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences, NLP is also used in many consumer products.
NLP can also help streamline business processes, increase employee productivity, and simplify mission-critical business processes with enterprise solutions.
NLP tasks
This is a fact that human language is very contradicting which makes it really chaotic to write software that determines the exact meaning of text or voice data perfectly. Exceptions to grammar and usage, homophones, idioms, metaphors, sarcasm, idiomatic expressions, variations in sentence structure — these are only a few examples of the irregularities inherent in human language that take humans years to learn, yet that programmers must teach applications designed to handle natural language to recognize and understand accurately from the start.
Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it’s ingesting. Some of these tasks include the following:
- Speech Recognition, also known as speech-to-text, is the task of reliability converting voice data into text data. Any application that follows voice commands or answers spoken questions requires speech recognition. The way people speak makes speech recognition particularly challenging: they speak quickly, slur words together, change their emphasis and intonation, and often use incorrect grammar.
- Part-of-speech tagging, also known as grammatical tagging, refers to the process of identifying the part of speech of a particular word or sentence based on its use and context. Part of speech classifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make of car do you own?’
- A process of semantic analysis determines the meaning of a word with multiple meanings through the process of word sense disambiguation. In the following example, word sense disambiguation helps distinguish the meaning of ‘make’ in ‘make the grade’ (achieve) from ‘make a bet’ (place).
- In named entity recognition, or NEM, words or phrases are identified as useful entities. The NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a person.
- Identifying it and when two words refer to the same entity is called co-reference resolution.
- The purpose of the sentiment analysis is to extract subjective qualities-attitudes, emotions, sarcasm, confusion, suspicion-from the text.
- Often, describtion of natural language generation is the opposite of speech recognition or speech-to-text; it’s the task of translating structured information into human language.
NLP tools and approaches
Python and the Natural Language Toolkit (NLTK)
The Python Programming Language offers a wide range of tools and libraries for attacking particular NLP Tasks. Most of them arein the Natural Language Toolkit. It is an open-source collection of libraries, programs, and education resources for curating the NLP Program.
The NLTK offers libraries for many of the above-mentioned NLP tasks, as well as libraries for subtasks like sentence parsing, word segmentation, stemming and lemmatization (word-trimming methods), and tokenization (for breaking phrases, sentences, paragraphs, and passages into tokens that help the computer better understand the text). It also offers libraries for developing capabilities like semantic reasoning, which allows users to draw logical inferences based on information retrieved from the text.
Statistical NLP, machine learning, and deep learning
The first NLP applications were hand-coded, rules-based systems that could do some NLP tasks but couldn’t easily expand to meet an ever-increasing stream of exceptions or the growing volumes of text and voice input.
Statistical natural language processing (NLP) uses computer algorithms with machine learning and deep learning models to automatically extract, classify, and label parts of text and speech input, and then assign a statistical likelihood to each probable meaning. Deep learning models and learning approaches based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) now allow NLP systems to ‘learn’ as they go, extracting ever more accurate meaning from massive amounts of unstructured, unlabeled text and voice input.
NLP Use Cases
In many modern real-world applications, natural language processing is the driving force behind machine intelligence. Listed below are a few examples:
Spam detection
Although you would not think of spam detection as an NLP solution, the top spam detection solutions examine emails for language that commonly suggests spam or phishing. Overuse of financial words, typical bad grammar, threatening language, improper urgency, misspelled corporate names, and other signs can all be used as indicators. Although you may object that this does not match your email experience, spam detection is one of a handful of NLP problems that experts consider mostly solved.’
Machine translation
Google Translate is a good illustration of how NLP technology is useful in the workplace. More than just substituting words in one language with words in another is required for truly useful machine translation. The effective translation must precisely capture the meaning and tone of the input language and translate it to text in the output language with the same meaning and desired impact. In terms of accuracy, machine translation systems are making good progress. Translating text to one language and then back to the original is an excellent approach to test any machine translation algorithm. A well-known classic example: translating “The spirit is willing but the flesh is weak” from English to Russian and back resulted in “The vodka is wonderful but the meat is nasty” not long ago. Today, the outcome is “The spirit craves, but the flesh is weak,” which isn’t flawless but gives the English-to-Russian translation a lot more confidence.
Virtual agents and chatbots
Virtual agents like Apple’s Siri and Amazon’s Alexa Speech uses ecognition and natural language generation byto recognize patterns in voice commands and respond with the appropriate action or helpful comments. In response to typed text entries, chatbots work the same magic. The best of them also learn to understand contextual hints in human requests. And utilize them to improve their responses or possibilities over time. The capacity to react to our questions—anticipated or not—with meaningful and helpful replies in their own words is the next development for these applications.
Social media sentiment analysis
For revealing hidden data insights from social media platforms, natural language processing (NLP) has become an indispensable commercial tool. Sentiment analysis may extract attitudes and emotions in response to products, promotions, and events by analyzing the language used in social media postings, answers, reviews, and more–information that organizations can utilize in product design, advertising campaigns, and more.
Text summarization
Text summarization makes use of natural language processing (NLP) techniques to digest large amounts of digital text. And provide summaries and synopses for indexes, research databases, and busy users who don’t have time to read the entire text. To provide valuable context and conclusions to summaries, the finest text summarising applications use semantic reasoning and natural language generation (NLG).