Natural language processing: Difference between revisions

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{{About|computer processing|human brain processing|Language processing in the brain}}

{{More citations needed|date=May 2024}}

'''Natural language processing''' ('''NLP''') is a subfield of [[computer science]] and especially [[artificial intelligence]]. It is primarily concerned with providing computers with the ability to process data encoded in [[natural language]] and is thus closely related to [[information retrieval]], [[knowledge representation]] and [[computational linguistics]], a subfield of [[linguistics]].

Major tasks in natural language processing are [[speech recognition]], [[text classification]], [[natural-language understanding]], and [[natural language generation|natural-language generation]].

=== 1. What is NLP? ===

Natural Language Processing (NLP) is a specialized branch of '''computer science and artificial intelligence'''. Its main goal is to enable computers to understand, interpret, and generate human language, just like people do. Instead of relying on programming languages or code, NLP focuses on processing '''natural languages'''—like English, Hindi, or Telugu—that humans use every day.

=== 2. Purpose of NLP ===

The core idea behind NLP is to make communication between humans and machines smoother and more natural. This involves teaching machines how to read, listen to, and even respond to text or speech in ways that are meaningful. It connects deeply with areas such as '''information retrieval''' (like search engines), '''knowledge representation''' (storing meaning and facts), and '''computational linguistics''' (applying linguistics through computers).

=== 3. Key Goals and Challenges ===

NLP is not just about translating words; it’s about understanding '''context, grammar, and meaning'''. For example, the word “bank” can mean a financial institution or the edge of a river. NLP systems must learn to figure out which meaning fits based on context. This makes the task both powerful and challenging—because human language is full of '''ambiguity, slang, and emotion'''.

=== 4. Major Tasks in NLP ===

There are several major tasks in NLP that help achieve these goals:

* '''Speech Recognition''': Converting spoken words into text (used in voice assistants like Siri).

* '''Text Classification''': Grouping or tagging text into categories (like spam vs. non-spam emails).

* '''Natural Language Understanding (NLU)''': Helping machines understand the meaning behind words and phrases.

* '''Natural Language Generation (NLG)''': Enabling machines to produce text or speech that sounds natural.

=== 5. Applications in the Real World ===

NLP is used in everyday technology—like Google Translate, Alexa, Grammarly, and chatbots on websites. It helps in sentiment analysis (e.g., analyzing customer reviews), document summarization, and even in healthcare to process medical records. Its applications are growing rapidly in fields such as education, law, marketing, and customer service.

=== 6. The Future of NLP ===

With the rise of '''machine learning and deep learning''', NLP is becoming even more powerful. Models like ChatGPT and BERT can understand and generate human-like responses. As research continues, NLP is expected to play a bigger role in making AI systems more intelligent, ethical, and responsive to human needs.

== History ==

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*'''2003:''' [[word n-gram language model|word n-gram model]], at the time the best statistical algorithm, is outperformed by a [[multi-layer perceptron]] (with a single hidden layer and [[context length]] of several words, trained on up to 14 million words, by [[Yoshua Bengio|Bengio]] et al.)<ref>{{Cite journal|url=https://dl.acm.org/doi/10.5555/944919.944966|title=A neural probabilistic language model|first1=Yoshua|last1=Bengio|first2=Réjean|last2=Ducharme|first3=Pascal|last3=Vincent|first4=Christian|last4=Janvin|date=March 1, 2003|journal=The Journal of Machine Learning Research|volume=3|pages=1137–1155|via=ACM Digital Library}}</ref>

*'''2010:''' [[Tomáš Mikolov]] (then a PhD student at [[Brno University of Technology]]) with co-authors applied a simple [[recurrent neural network]] with a single hidden layer to language modelling,<ref>{{cite book |last1=Mikolov |first1=Tomáš |last2=Karafiát |first2=Martin |last3=Burget |first3=Lukáš |last4=Černocký |first4=Jan |last5=Khudanpur |first5=Sanjeev |title=Interspeech 2010 |chapter=Recurrent neural network based language model |journal=Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010 |date=26 September 2010 |pages=1045–1048 |doi=10.21437/Interspeech.2010-343 |s2cid=17048224 |chapter-url=https://gwern.net/doc/ai/nn/rnn/2010-mikolov.pdf |language=en}}</ref> and in the following years he went on to develop [[Word2vec]]. In the 2010s, [[representation learning]] and [[deep learning|deep neural network]]-style (featuring many hidden layers) machine learning methods became widespread in natural language processing. That popularity was due partly to a flurry of results showing that such techniques<ref name="goldberg:nnlp17">{{cite journal |last=Goldberg |first=Yoav |year=2016 |arxiv=1807.10854 |title=A Primer on Neural Network Models for Natural Language Processing |journal=Journal of Artificial Intelligence Research |volume=57 |pages=345–420 |doi=10.1613/jair.4992 |s2cid=8273530 }}</ref><ref name="goodfellow:book16">{{cite book |first1=Ian |last1=Goodfellow |first2=Yoshua |last2=Bengio |first3=Aaron |last3=Courville |url=http://www.deeplearningbook.org/ |title=Deep Learning |publisher=MIT Press |year=2016 }}</ref> can achieve state-of-the-art results in many natural language tasks, e.g., in [[language modeling]]<ref name="jozefowicz:lm16">{{cite book |first1=Rafal |last1=Jozefowicz |first2=Oriol |last2=Vinyals |first3=Mike |last3=Schuster |first4=Noam |last4=Shazeer |first5=Yonghui |last5=Wu |year=2016 |arxiv=1602.02410 |title=Exploring the Limits of Language Modeling |bibcode=2016arXiv160202410J }}</ref> and parsing.<ref name="choe:emnlp16">{{cite journal |first1=Do Kook |last1=Choe |first2=Eugene |last2=Charniak |journal=Emnlp 2016 |url=https://aclanthology.coli.uni-saarland.de/papers/D16-1257/d16-1257 |title=Parsing as Language Modeling |access-date=2018-10-22 |archive-date=2018-10-23 |archive-url=https://web.archive.org/web/20181023034804/https://aclanthology.coli.uni-saarland.de/papers/D16-1257/d16-1257 |url-status=dead }}</ref><ref name="vinyals:nips15">{{cite journal |last1=Vinyals |first1=Oriol |last2=Kaiser |first2=Lukasz |display-authors=1 |journal=Nips2015 |title=Grammar as a Foreign Language |year=2014 |arxiv=1412.7449 |bibcode=2014arXiv1412.7449V |url=https://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf }}</ref> This is increasingly important [[artificial intelligence in healthcare|in medicine and healthcare]], where NLP helps analyze notes and text in [[Electronic health record|electronic health records]] that would otherwise be inaccessible for study when seeking to improve care<ref>{{Cite journal|last1=Turchin|first1=Alexander|last2=Florez Builes|first2=Luisa F.|date=2021-03-19|title=Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review|journal=Journal of Diabetes Science and Technology|volume=15|issue=3|language=en|pages=553–560|doi=10.1177/19322968211000831|pmid=33736486|pmc=8120048|issn=1932-2968}}</ref> or protect patient privacy.<ref>{{Cite journal |last1=Lee |first1=Jennifer |last2=Yang |first2=Samuel |last3=Holland-Hall |first3=Cynthia |last4=Sezgin |first4=Emre |last5=Gill |first5=Manjot |last6=Linwood |first6=Simon |last7=Huang |first7=Yungui |last8=Hoffman |first8=Jeffrey |date=2022-06-10 |title=Prevalence of Sensitive Terms in Clinical Notes Using Natural Language Processing Techniques: Observational Study |journal=JMIR Medical Informatics |language=en |volume=10 |issue=6 |pages=e38482 |doi=10.2196/38482 |issn=2291-9694 |pmc=9233261 |pmid=35687381 |doi-access=free }}</ref>

=== Applications of Natural Language Processing ===

Natural Language Processing (NLP) has become an essential technology across a broad range of industries. As deep learning and large-scale language models have evolved, the capabilities of NLP systems have significantly improved, enabling automation, insight generation, and efficient data interaction in both consumer-facing and enterprise environments.

'''1. Customer Service and Chatbots'''

NLP enables the development of intelligent virtual assistants and chatbots that can understand and respond to customer inquiries in real time. These systems are trained to interpret natural language input, identify the user's intent, and deliver helpful responses. They are widely used in e-commerce platforms, telecommunications, banking, and other service industries to streamline support, reduce wait times, and operate 24/7. In addition to answering queries, these chatbots can also handle appointment scheduling, order tracking, and complaint resolution.

'''2. Healthcare'''

In the healthcare sector, NLP is used to process large volumes of unstructured clinical data, such as doctors’ notes and patient records. By extracting meaningful insights from text, NLP helps in generating summaries, identifying medical conditions, and flagging potential risks. It plays a critical role in improving patient care, supporting diagnosis, and even predicting health trends. NLP is also used to automate literature reviews, mine clinical trial data, and support medical research.

'''3. Legal and Compliance'''

Legal professionals use NLP tools to analyze contracts, extract key clauses, and identify regulatory risks. These systems assist in reviewing large volumes of legal text much faster than manual efforts, improving accuracy and consistency. In compliance, NLP helps monitor communication, flag policy violations, and ensure documentation meets legal standards. This reduces the administrative burden on law firms and helps maintain regulatory integrity.

'''4. Content Moderation and Sentiment Analysis'''

Social media platforms and online communities use NLP to automatically detect offensive language, hate speech, spam, and other forms of inappropriate content. Sentiment analysis, a specific NLP task, allows businesses and researchers to analyze text data and gauge public opinion about products, policies, or events. By understanding sentiment, companies can respond proactively to customer feedback or manage brand reputation. These tools are essential for maintaining safe online environments and understanding user behavior.

'''5. Education and Accessibility'''

NLP is transforming education through automated grading systems, text summarization, and personalized learning platforms that adapt content based on student performance. It also supports learners with disabilities by providing speech-to-text and text-to-speech tools, helping those with visual or hearing impairments. Intelligent tutoring systems use NLP to simulate one-on-one teaching experiences by interpreting student input and providing targeted feedback. In academic research, NLP assists in synthesizing literature and extracting relevant insights.

'''6. Translation and Cross-Language Understanding'''

Machine translation systems like Google Translate rely on NLP to convert text from one language to another while preserving meaning and context. These systems help break language barriers, enabling communication in multinational organizations, travel, and global collaboration. NLP-based translation is used in mobile apps, websites, and email clients, and has expanded access to education, media, and services across the globe. Newer NLP models can even understand cultural nuances and idiomatic expressions, making translations more accurate and human-like.

==Approaches: Symbolic, statistical, neural networks{{anchor|Statistical natural language processing (SNLP)}} ==