Natural Language Processing Use Cases

What exactly is natural language processing (NLP)? Natural language processing (NLP) is a section of computer science—specifically, artificial intelligence or AI—concerned with enabling computers interpret text and spoken words in the same manner that humans do.


NLP blends computational linguistics (human language rule-based modeling) with statistical, deep learning and machine learning models. A combination of these technologies allows computers to process human language as text or voice data and 'understand' its full meaning, complete with the writer's or speaker's sentiment and intent. NLP powers computer programs to convert text from one language to another, reply to spoken commands, and quickly summarize vast amounts of material—even in real time. You've probably encountered NLP as voice-activated GPS systems, speech-to-text dictation software, digital assistants, among other scenarios. However, NLP is increasingly being used in corporate solutions to assist expedite business operations, boost employee productivity, and simplify mission-critical business procedures.


NLP Tasks


The uncertainties in human language make it extremely difficult to build a software that properly identifies the intended meaning of text or speech input. Homonyms, sarcasm, homophones, idioms, grammar, metaphors and sentence structure variations are a few of the human language irregularities that take individuals years to understand, but that developer must teach natural language-driven apps to recognize and understand precisely from the start if those apps are to be feasible.


NLP tasks deconstruct human text and speech data to assist the machine in making sense of what it is absorbing. Among these tasks are:



• Speech-to-text involves consistently turning voice input into text data. The process is also known as speech recognition. Speech recognition is necessary for any application that responds to voice commands or enquiries. The way individuals communicate makes speech recognition exceptionally difficult—quickly, slurring words together, with varied emphasis and intonation, in diverse dialects, and frequently using improper grammar.

• Part of speech tagging is the technique of detecting a word's or piece of text's part of speech based on its use and context. 'Make' is used as a verb in 'I can make a paper aircraft,' and as a noun in 'What make of automobile do you own?'.
• Word sense disambiguation is determining the meaning of a word with several meanings using semantic analysis to discover which word makes the most sense in the current context. Word sense disambiguation, for example, aids in distinguishing the definition of the verb 'make' in 'make the grade' vs.'make a bet' (place).
• NEM, or named entity recognition, recognizes words or phrases as useful entities. NEM helps recognizes 'Kentucky' as a place or 'Fred' as a man's name.
• Co-reference resolution involves determining whether two words refer to the same item. The most typical example is recognizing the person or thing to whom a pronoun refers, but it may also include identifying a metaphor or an idiom in the text (e.g., an instance in which 'bear' refers to a giant hairy person rather than an animal).
• Sentiment analysis seeks to extract subjective aspects of text, such as attitudes, emotions, sarcasm, bewilderment, and suspicion.
• Natural language generation is frequently referred to as the inverse of voice recognition or speech-to-text and it involves converting structured data into human language.

NLP Tools and Approaches


Natural Language Toolkit and Python (NLTK)


The Python programming language includes a plethora of tools and packages for tackling specialized NLP problems. Many of them are included in the Natural Language Toolkit, or NLTK, which is an open source collection of libraries, applications, and educational materials for developing NLP systems.


The NLTK offers libraries for many of the above NLP tasks, as well as libraries for subtasks like sentence parsing, stemming, word segmentation, and lemmatization (techniques of cutting words down to their roots), and tokenization (for breaking phrases, paragraphs, sentences, and passages into units that help the computer better understand the text). 


Statistical natural language processing, machine learning, and deep learning


The first NLP apps were hand-coded, rule-based structures that could do certain NLP tasks but couldn't, readily grow to meet an apparently infinite stream of exceptions or rising amounts of text and speech input.


Statistical natural language processing (NLP)  incorporates computer algorithms with deep learning and machine learning models to extract, categorize, and label parts of text and speech input before assigning a statistical likelihood to each interpretation of those elements. Deep learning methods and learning models based on CNNs and RNNs now allow NLP structures to 'learn' as they operate and extract more definite meaning from great amounts of unstructured, raw, and unlabeled speech and text data.


Examples of NLP Use Cases


In most current real-world operations, NLP is the force behind machine intelligence. Here are a couple of examples:



• Spam detection: You might not think of spam detection as a natural language processing solution, but the primary spam detection solutions check emails for language that indicates spam or phishing. Overuse of financial jargon, typical poor grammar, aggressive tone, improper urgency, misspelled corporate names, and other factors might all be signs. Spam detection is one of a few NLP topics that experts regard to be "largely solved" (even though this does not correspond to your email experience).
• Machine translation: Google Translate is an example of NLP technology in action. True machine translation entails more than simply replacing words in one language with words in another. Efficient translation must accurately capture the tone and meaning of the input language and convert it to text in the output language with the same meaning and desired impact. Machine translation techniques are improving in terms of accuracy. A fantastic approach to put any machine translation program to the test is to translate text from one language to another and then back again. A well-known classic example: Translation of "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 bad".
• Chatbots and virtual agents: Virtual assistants like Apple's Siri and Amazon's Alexa employ speech recognition and natural language generation to reply with helpful comments. Chatbots work the same magic in response to text input. The finest of these also learn to understand contextual cues in human requests and utilize them to deliver better replies or alternatives. The next improvement for these programs will be question answering, which will allow them to react to inquiries, whether or not expected, with relevant and helpful replies.
• Social media sentiment analysis: Natural language processing (NLP) has evolved into an essential commercial tool for identifying hidden data insights from social media channels. Sentiment study may extract emotions and attitudes in reaction to goods, events and promotions by analyzing language used in social media postings, answers, reviews, and more—information that businesses can utilize in product design, advertising campaigns, and more.
• Text summarizing: Text summarization use NLP approaches to digest massive amounts of digital text and provide summaries and synopses for indexes, research databases, and busy readers who do not have time to read complete text. To provide valuable context and conclusions to summaries, the finest text summarizing programs utilize semantic reasoning and natural language generation (NLG).

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