Hi guys, so today I am going to start a series of articles on NLP(Natural Language Processing). I will be writing about what is NLP, some basic tasks of NLP, its usages and the various algorithms used for NLP. I will also include some tools which you can use and see the power of NLP. I will also be covering some of the most commonly used NLP algorithms in this series.
Its going to be a fun and insightful series where we will learn the theoritical concepts as well as pratical implementation of the most common NLP algorithms. So stay with me if you want to know more about the power of NLP and how to use it.
So, lets begin our NLP journey.
Introduction to NLP
Natural language processing strives to build machines that understand and respond to text or voice data—and respond with text or speech of their own—in much the same way humans do.
Natural language processing (NLP) is a branch of artificial intelligence (AI) that helps computers understand, interpret and manipulate human language. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
Some common tasks in NLP
Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure—these just a few of the irregularities of human language that take humans years to learn, but that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to be useful.
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:
1. Speech recognition also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar.
2. Part of speech (PoS) tagging also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context. Part of speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make of car do you own?’
3. Word sense disambiguation is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
4. Named entity recognition (NER) identifies words or phrases as useful entities. NER identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
5. Co-reference resolution is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers (e.g., ‘she’ = ‘Mary’), but it can also involve identifying a metaphor or an idiom in the text (e.g., an instance in which 'bear' isn't an animal but a large hairy person).
6. Sentiment analysis attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
7. Natural language generation is sometimes described as the opposite of speech recognition or speech-to-text; it's the task of putting structured information into human language.
Where is NLP used
Natural language processing is the driving force behind machine intelligence in many modern real-world applications. Here are a few examples:
- Spam detection: You may not think of spam detection as an NLP solution, but the best spam detection technologies use NLP's text classification capabilities to scan emails for language that often indicates spam or phishing. These indicators can include overuse of financial terms, characteristic bad grammar, threatening language, inappropriate urgency, misspelled company names, and more. Spam detection is one of a handful of NLP problems that experts consider 'mostly solved' (although you may argue that this doesn’t match your email experience).
- Machine translation: Google Translate is an example of widely available NLP technology at work. Truly useful machine translation involves more than replacing words in one language with words of another. Effective translation has to capture accurately the meaning and tone of the input language and translate it to text with the same meaning and desired impact in the output language. Machine translation tools are making good progress in terms of accuracy. A great way to test any machine translation tool is to translate text to one language and then back to the original. An oft-cited classic example: Not long ago, translating “The spirit is willing but the flesh is weak” from English to Russian and back yielded “The vodka is good but the meat is rotten.” Today, the result is “The spirit desires, but the flesh is weak,” which isn’t perfect, but inspires much more confidence in the English-to-Russian translation.
- Virtual assistants and chatbots: Virtual assistants such as Apple's Siri and Amazon's Alexa use speech recognition to recognize patterns in voice commands and natural language generation to respond with appropriate action or helpful comments. Chatbots perform the same magic in response to typed text entries. The best of these also learn to recognize contextual clues about human requests and use them to provide even better responses or options over time. The next enhancement for these applications is question answering, the ability to respond to our questions—anticipated or not—with relevant and helpful answers in their own words.
- Social media sentiment analysis: NLP has become an essential business tool for uncovering hidden data insights from social media channels. Sentiment analysis can analyze language used in social media posts, responses, reviews, and more to extract attitudes and emotions in response to products, promotions, and events–information companies can use in product designs, advertising campaigns, and more.
- Text summarization: Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, or busy readers who don't have time to read full text. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add useful context and conclusions to summaries.
Some famous tools for NLP
Basically, you can start using NLP tools through SaaS (software as a service) tools or open-source libraries.
SaaS tools are ready-to-use and powerful cloud-based solutions that can be implemented with low or no code. SaaS platforms often offer pre-trained NLP models that can be used code-free, and APIs that are geared more towards those who want a more flexible, low-code, option, e.g. professional developers, or those learning to code, who want to simplify their work. If you are looking to implement NLP in a way that’s cost-effective and quick, SaaS tools are the way to go!
Open-source libraries, on the other hand, are free, flexible, and allow you to fully customize your NLP tools. They are aimed at developers, however, so they’re fairly complex to grasp and you will need experience in machine learning to build open-source NLP tools. Luckily, though, most of them are community-driven frameworks, so you can count on plenty of support.
To build your own NLP models with open-source libraries, you’ll need time to build infrastructures from scratch, and you’ll need money to invest in devs if you don’t already have an in-house team of experts.
Some famous NLP tools are:
MonkeyLearn : a user-friendly, NLP-powered platform that helps you gain valuable insights from your text data.
Aylien: is a SaaS API that uses deep learning and NLP to analyze large volumes of text-based data, such as academic publications, real-time content from news outlets and social media data.
IBM Watson: is a suite of AI services stored in the IBM Cloud. One of its key features is Natural Language Understanding, which allows you to identify and extract keywords, categories, emotions, entities, and more.
Google Cloud NLP API: provides several pre-trained models for sentiment analysis, content classification, and entity extraction, among others. Also, it offers AutoML Natural Language, which allows you to build customized machine learning models.
Amazon Comprehend: is an NLP service, integrated with the Amazon Web Services infrastructure. You can use this API for NLP tasks such as sentiment analysis, topic modeling, entity recognition, and more.
NLTK: is one of the leading tools in NLP model building. Focused on research and education in the NLP field, NLTK is bolstered by an active community, as well as a range of tutorials for language processing, sample datasets, and resources that include A comprehensive Language Processing and Python handbook.
Stanford Core NLP: is a popular library built and maintained by the NLP community at Stanford University. It’s written in Java ‒ so you’ll need to install JDK on your computer ‒ but it has APIs in most programming languages.
TextBlob: is a Python library that works as an extension of NLTK, allowing you to perform the same NLP tasks in a much more intuitive and user-friendly interface. Its learning curve is more simple than with other open-source libraries, so it’s an excellent choice for beginners, who want to tackle NLP tasks like sentiment analysis, text classification, part-of-speech tagging, and more.
SpaCy: an open-source NLP with Python library which is lightning-fast, easy to use, well-documented, and designed to support large volumes of data and not to mention, boasts a series of pretrained NLP models that make your job even easier. Unlike NLTK or CoreNLP, which display a number of algorithms for each task, SpaCy keeps its menu short and serves up the best available option for each task at hand.
GenSim: Gensim is a highly specialized Python library that largely deals with topic modeling tasks using algorithms like Latent Dirichlet Allocation (LDA). It’s also excellent at recognizing text similarities, indexing texts, and navigating different documents.
Final thoughts
I hope this article has given you a boost to start learning NLP and see its amazing power and influence in today's worls with a more deeper sense and understanding. It is a vast and young field, and over the past few years, Deep Learning architectures and algorithms have made impressive advances, yielding state-of-the-art results for some common NLP tasks.
In my next post, I will be continuing this series and we will be looking at some NLP tasks like Tokenization, Stemming etc with their pratical implementation.
Till than, you can play around with the tools I mentioned above and see how cool NLP really is.
Do not hesitate to share your experience with natural language processing and how you think it can help you in the comments section. And please feel free to share with me books, websites, works and tools that you consider important and that I do not mention here.
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