Natural language processing (NLP) is a branch of artificial intelligence that allows computers to understand text and spoken words in the same way humans can. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. Together, these technologies enable computers to process human language in text or voice data and ‘understand’ its whole meaning, complete with the speaker or writer’s intent and sentiment. NLP has existed for more than 50 years and has roots in linguistics. It has various real-world applications in many fields, including medical research, search engines, and business intelligence.

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- How does Natural Language Processing work?
- Why is Natural Language Processing important?
- Natural Language Processing use cases.

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How does Natural Language Processing work?

Breaking down the elemental pieces of language

Natural language processing includes various techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection, and identification of semantic relationships. In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces, and explore how the pieces work together to create meaning. These underlying tasks are often used in higher-level NLP capabilities, such as:

- Content categorization: A linguistic-based document summary, including search and indexing, content alerts, and duplication detection.
- Topic discovery and modeling: Accurately capture the meaning and themes in text collections and apply advanced analytics to text, like optimization and forecasting.
- Contextual extraction: Automatically pull structured information from text-based sources.
- Sentiment analysis: Identifying the mood or subjective opinions within large amounts of text, including average sentiment and opinion mining.
- Speech-to-text and text-to-speech conversion: Transforming voice commands into written text, and vice versa.
- Document summarization: Automatically generating synopses of large bodies of text.
- Machine translation: Automatic translation of text or speech from one language to another.
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- In all these cases, the overarching goal is to take raw language input and use linguistics and algorithms to transform or enrich the text to deliver more excellent value.

Why is Natural Language Processing important?

- Large volumes of textual data

Natural language processing helps computers communicate with humans in their language and scales other language-related tasks. For example, NLP allows computers to read text, hear speech, interpret it, measure sentiment, and determine which parts are essential.

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Today’s machines can analyze more language-based data than humans without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to analyzing text and speech data efficiently and comprehensively.

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- Structuring a highly unstructured data source

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms, and slang. When we write, we often misspell or abbreviate words or omit punctuation. When we speak, we have regional accents, mumble, stutter, and borrow terms from other languages.

While supervised and unsupervised learning, specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

Natural Language Processing use cases.

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 spam detection is an NLP solution. However, the best spam detection technologies use NLP’s text classification capabilities to scan emails for language that often indicates spam or phishing. 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. Handy machine translation involves more than replacing words in one language with words from another. Effective translation has to capture the meaning and tone of the input language accurately and translate it to text with the same purpose 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.
- Virtual agents and chatbots: Virtual agents 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 the 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.
- Social media sentiment analysis: NLP has become an essential business tool for uncovering hidden data insights from social media channels. Sentiment analysis can analyze the 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 it 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 the full text. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add helpful context and conclusions to summaries.