In this post, we are going to explore the NLP usability and use case as well as what does natural language processing do? indifferent industries.
Natural language processing (NLP) has been an active area of computer science research since at least the 1960s.
NLP software can be used to perform many different tasks, including automatic summarization and translation between languages.
Natural language processing software tools also have numerous practical applications in the fields of computational linguistics, speech recognition, machine translation, information retrieval, text mining, and others.
This article describes what natural language processing software does and how it works, as well as explores the history of natural language processing and some of its applications today.
What is Natural Language Processing?
Natural Language Processing (NLP) refers to an effort to build machines that understand and respond to text or voice data and respond with text or speech of their own.
In many ways, NLP involves natural language understanding, though there are also strong components of artificial intelligence and machine learning involved in these efforts.
Generally speaking, NLP is closely tied to other fields such as computational linguistics and information retrieval.
However, because it deals with natural languages like English and Chinese rather than computer programming languages like Java or C++, it’s perhaps more accessible than some of those other specialized areas of computer science.
There’s quite a bit more to NLP than mere word-by-word analysis; what matters most are things like context and sentiment analysis, which means understanding how words fit together in different combinations and identifying key messages based on larger chunks of sentences.
Related Article: Top 10 Natural Language Processing Tools | Text Analysis
What is Natural Language Understanding?
The primary goal of natural language understanding is to capture meaning in text and speech.
Understanding enables a computer or robot to not only process a user’s command but also be able to respond with their own text and/or speech.
For example, when asked, How far is it from here to Springfield?, an NLP program would be able to recognize that here refers to your current location, whereas Springfield likely refers to a city name.
Based on that understanding, as well as other details such as your previous navigation history and map data, an automated system could generate contextually relevant results to answer your question: At 3 hours 13 minutes away by car.
Alternatively, if you were about to leave for Springfield in 1 hour 20 minutes by train, you might receive different results: It takes 2 hours 12 minutes by car; you should leave now so you don’t miss your ride!
What is Natural Language Generation?
You might also be familiar with NLP’s counterpart, natural language generation (NLG). This is where machines are built to convert data into human-readable text.
NLG is a particularly useful technology in areas like content marketing and customer communications because it allows you to write once in other words, generate multiple types of communication from one piece of content.
However, NLP remains in its infancy compared to AI technologies such as image recognition or machine learning.
It should also be noted that certain industries, including law enforcement and finance, have rules around how natural language can be used.
For example, chatbots attempting to pass themselves off as humans could cause legal problems for their creators.
NLP with Sentiment Analysis
Natural language processing coupled with sentiment analysis can be used to gauge public opinion about specific topics or products.
According to IBM, Sentiment analysis is a type of text analytics that determines whether a given piece of writing is positive, negative, or neutral.
The method involves examining individual words in a sentence and determining whether they are associated with positive or negative feelings.
To get started with NLP + Sentiment Analysis, try combining your Python programming skills with one of these free libraries NLTK.
Natural Language Toolkit (NLTK) is an open-source library for natural language processing tasks including part-of-speech tagging, named entity recognition, clustering analysis, and parsing.
This toolkit is useful for any programmer interested in NLP as it allows you to find patterns within large volumes of unstructured data.
Related Article: Sentiment Analysis: Comprehensive Guide on NLP
Speech Recognition and Synthesis
As humans, we’re pretty good at doing three things with text: recognizing it, synthesizing it, and interacting with it.
Speech recognition and synthesis are both natural extensions of these skills and are necessary for machines to truly interact with us in a humanlike way.
Essentially, what you’re getting from speech recognition software is an interface between humans and technology that isn’t dominated by keyboard and screen.
Instead, you speak to your computer (or phone) and have your words converted into digital format; with speech synthesis, those words are translated back into sound.
This may not seem like much on its own but combined with improved NLP techniques software understanding our phrases better than ever before these technologies can begin bringing a wholly new level of convenience to our lives.
NLP Use Cases
The NLP function has a wide range of uses. It can be used to build search engines that extract information from large bodies of text or to train personal assistants like Apple’s Siri to respond appropriately and intelligently in conversation with human users.
It can also be employed for sentiment analysis, where it attempts to understand how people feel about certain products or companies and then use that information in strategies for sales, marketing, and public relations.
While there are many potential ways to apply natural language processing to a business, one area in which it has been largely underutilized is customer service.
Instead of using old-fashioned FAQ pages on their websites or relying on live chat software both methods that force customers into predictable question-and-answer formats companies could be using NLP as an advanced tool for customer support agents.
NLP Tools and Approaches
The tools and approaches used to create NLP will depend on your data set, its purpose, and what you hope to get out of it. Here are some of them
1. Phonetic Tagging and Pronouncing
How can I make my computer understand sounds (phonetics)? Maybe by using Markov models or other methods for understanding phonetics and how words sound when we speak them and translating those pronunciations into text or mapping them to categories like alliteration or assonance. or whatever metaphor seems apt in a particular situation, This approach is often seen in music analysis and literature.
2. Semantic Tagging and Defining
How can I get computers to define words? An early method involved creating dictionaries that would pair each word with another word, depending on context; e.g., the sort might be paired with numbers if it appears near sort numbers rather than sort laundry
3. Question Answering
Can computers answer questions? Yes, they can! text analysis and semantics analysis helps in this approach, and it generates answers based on semantics to every question.
4. Speech Recognition and Understanding
How can I give my computer voice? Speech recognition software is currently a very useful tool for speech therapists and educators who must use time efficiently.
The ability to process text or voice data, along with its variations, on a large scale enables us to better understand natural language.
Speech recognition technology can help find patterns in data that might be missed when simply reviewing it at a human level.
Moreover, detecting patterns allows us to generalize about more than what’s in front of us: we can learn from one example and apply that learning to new situations.
5. Text-to-Text Translation:
When Google Translate was initially released back in 2001, many experts saw speech recognition as crucial to getting their next step teaching machines to translate one language into another
6. Statistical Parsing of Natural Language –
What’s statistical parsing? It’s a way of generating meaning from text without necessarily understanding what has been said (or written) which is not much different from keyword search.
Statistical parsing assigns probabilities to possible meanings according to statistics built up over time
7. Machine Learning:
Building systems that can learn new things without human intervention 8. Creating question-answering systems
Natural Language Processing Tasks
Natural Language Processing, or NLP, is a subset of artificial intelligence that’s intended to give computers human-like communication skills. NLP can be applied to many different tasks and domains.
These natural language processing tasks include:
1. Sentiment Analysis – Analyzing text or speech for positive or negative mood and attitude; determining if what’s being communicated is positive, negative, about a person, about a product or service, etc.
2. Word Sense Disambiguation – Figuring out which sense of a word is meant in context with other words (for example, seeing that big means big in size, not importance).
3. Text Analytics – Using natural language to analyze structured data. For example, looking at sales trends based on seasonality or dates over time based on new social media campaigns.
4. Natural Language Generation (NLG) – Generating text from a template, often used by chatbots and digital assistants like Siri.
5. Speech Recognition – Translating audio into a machine-readable format using techniques such as Hidden Markov Models.
6. Speech Synthesis – Translating text into a voice using techniques such as concatenative synthesis or hidden Markov models.
7. Named Entity Recognition (NER) – Finding names of entities within blocks of text, typically people, places, and organizations but also products, software versions, cities anything you can name within a given sentence structure.
Deep Learning for Natural Language Processing
A Practitioner’s Perspective: Natural language processing (NLP) aims to automate tasks involving human languages, such as speech recognition and machine translation.
When you talk on your phone or chat with a friend, it seems almost magical that computers can process these exchanges and extract useful information.
To achieve such performance, NLP often relies on so-called deep learning methods artificial neural networks with many hidden layers which show good results for many NLP problems.
In recent years, much progress has been made in understanding how to build practical systems using deep learning approaches.
In fact, Google’s new Neural Machine Translation system is one of the best pieces of NLP ever built using purely unsupervised methods.
This success of deep learning spurred a great deal of interest among researchers and practitioners alike who sought to exploit similar techniques for their own applications.
This talk will provide an overview of recent trends and developments in several subfields of natural language processing where deep learning is making rapid advances, including sentiment analysis, dialogue management, question answering/retrieval, entity linking/anaphora resolution/referent disambiguation/sentiment analysis, sentence summarization, and machine translation.
Natural Language Processing (NLP) is a fascinating subfield of artificial intelligence (AI) in which researchers aim to design machines that understand and respond to text or voice data and respond with text or speech, the same way humans do.
Still, in its infancy, NLP has come a long way over just a few decades. From Google Translate’s English-to-French translation capabilities to IBM Watson’s Jeopardy!
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