Natural Language Processing: Difference between revisions

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Natural Language Processing

Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables computers to understand, interpret, and manipulate human language. It is an interdisciplinary domain that integrates concepts from linguistics, computer science, and cognitive psychology. The goal of NLP is to facilitate interaction between humans and machines through natural language, allowing for more intuitive and efficient communication.

Introduction

NLP encompasses a range of techniques aimed at processing and analyzing large amounts of natural language data. It includes tasks such as text analysis, sentiment analysis, speech recognition, language translation, and chatbot functionality. As the volume of text and spoken data generated worldwide increases, the importance and applications of NLP have grown significantly, influencing industries ranging from customer service to healthcare.

Natural Language Processing is crucial for developing applications that require a deep understanding of human language. It combines several areas of computer science, linguistics, and artificial intelligence with the objective of enabling machines to derive meaning from human language in a way that is similar to how people do.

History

The history of Natural Language Processing can be traced back to the early days of computing in the 1950s. Researchers began exploring the potential of machines to understand and generate human language. One of the earliest attempts was the development of the first machine translation systems, which aimed to automatically translate text from one language to another. The initial enthusiasm was met with challenges due to the complexities and ambiguities inherent in human language.

In the 1960s and 1970s, efforts in NLP advanced with the development of more sophisticated algorithms and the emergence of syntax-based approaches, such as formal grammars. Significant progress was made with the introduction of rule-based systems that applied linguistic rules to parse and generate language. However, these systems often struggled with the richness of natural language, leading to a wave of skepticism about the feasibility of NLP.

The 1980s and 1990s saw a shift towards statistical methods in NLP, encouraged by the increasing availability of large corpora of text and the advancement of computational power. Researchers began using probabilistic models and machine learning techniques to analyze language data, resulting in more effective solutions for tasks such as part-of-speech tagging and named entity recognition.

The early 21st century marked a significant turning point for NLP with the advent of deep learning. The development of neural networks enabled the creation of more complex models that could learn from vast amounts of textual data, leading to remarkable improvements in various NLP applications. Notable developments include recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and Transformer architectures, which have become foundational for modern NLP techniques.

Design and Architecture

The architecture of Natural Language Processing systems is generally based on a pipeline model that consists of several key components and stages. Each stage focuses on a specific aspect of language processing, incorporating various techniques from linguistics and machine learning.

Preprocessing

The first step in many NLP tasks involves preprocessing the text data. This stage typically includes:

Tokenization: The process of dividing text into individual words or tokens.
Normalization: This includes converting all text to lowercase and removing punctuation.
Stop-word Removal: The elimination of common words (such as "the", "and", "is") that may not contribute significant meaning.
Stemming and Lemmatization: Techniques aimed at reducing words to their base or root form, enhancing the analysis and comparison of similar terms.

Feature Extraction

After preprocessing, the next stage involves feature extraction, where the system converts text into a numerical format that machine learning algorithms can process. This process commonly employs methods such as:

Bag-of-Words Model: Represents text data as an unordered collection of words, neglecting grammar and order while capturing word frequency.
Term Frequency-Inverse Document Frequency (TF-IDF): A statistical measure that evaluates the importance of a word in a document relative to a corpus.
Word Embeddings: Techniques such as Word2Vec and GloVe represent words in a continuous vector space, capturing semantic relationships between words.

Models and Algorithms

At the core of NLP are several key models and algorithms that enable the processing and understanding of language. These include:

N-grams: Probabilistic models that take into account sequences of ‘n’ words to provide context and predict likely outcomes in language.
Hidden Markov Models (HMMs): Particularly used for tasks like part-of-speech tagging, where the system makes predictions about the current state based on previous states.
Neural Networks: Deep learning models, especially those based on the Transformer architecture, such as BERT and GPT, have revolutionized NLP by allowing for context-aware language understanding and generation.

Output Generation

Finally, the output generation stage produces the desired results from the processed data. This can involve:

Text Classification: Assigning categories to text, such as spam detection or sentiment analysis.
Language Generation: Creating new text based on the learned patterns from the input data, found in applications like chatbots and summarization programs.
Translation: Automatically translating text from one language to another with the help of models that understand nuance and context.

Usage and Implementation

Natural Language Processing has been widely adopted across various industries, demonstrating its versatility and applicability in real-world scenarios. Some key areas of implementation include:

Information Retrieval

NLP techniques facilitate the development of search engines and information retrieval systems that allow users to query vast amounts of textual data efficiently. By understanding user intent and context, these systems provide more relevant search results.

Sentiment Analysis

Companies utilize sentiment analysis to gauge public opinion about products, services, or events by analyzing customer reviews, social media posts, and online discussions. This analysis helps organizations make data-driven decisions and enhance customer satisfaction.

Chatbots and Virtual Assistants

NLP powers chatbots and virtual assistants, enabling them to understand and respond to user queries in natural language. These applications enhance customer service by providing instant responses and support, often used in sectors like e-commerce, banking, and healthcare.

Machine Translation

NLP allows for the development of machine translation systems that automatically translate text from one language to another, increasing accessibility to information across linguistic barriers. Advances in this area have improved the accuracy and fluency of translations, making them suitable for practical use.

Speech Recognition

Applications such as voice-activated assistants rely on NLP for speech recognition, which converts spoken language into text. This technology finds utility in various domains, including automotive systems, smart home devices, and customer service.

Real-world Examples

Specific implementations of Natural Language Processing in the real world demonstrate its capabilities and efficiency.

Google Translate

One of the most widely known applications of NLP is Google Translate, which employs deep learning techniques to provide real-time translation between multiple languages. By utilizing vast amounts of multilingual data, Google Translate continuously improves its accuracy and fluency.

Amazon Alexa

Amazon's Alexa is an example of an NLP-powered conversational agent that allows users to interact with devices using natural language. The system uses speech recognition and understanding algorithms to interpret user commands and respond accordingly.

IBM Watson

IBM Watson is a cognitive computing platform that leverages NLP to extract insights from unstructured data. Its applications range from healthcare diagnostics to customer support, enabling businesses to make informed decisions based on analyzed data.

Grammarly

Grammarly employs NLP to provide real-time writing assistance and grammar checking. By analyzing text for errors and suggesting improvements, Grammarly enhances writing quality and aids users in achieving effective communication.

Criticism and Controversies

While Natural Language Processing has made significant strides, it is not without its criticisms and challenges. Some of the key areas of concern include:

Bias in AI

NLP systems can inadvertently perpetuate biases present in the training data, leading to unjust outcomes in language generation and classification tasks. This issue raises ethical concerns about fairness, accountability, and transparency in AI-driven applications.

Misinterpretation of Context

Despite advancements in NLP technology, systems may still struggle to understand context, sarcasm, or nuances in human language. As a result, applications such as chatbots or virtual assistants may generate responses that are contextually inappropriate or misleading.

Security and Privacy Concerns

The collection and processing of large amounts of text data pose privacy risks. Models trained on sensitive information may inadvertently reveal personal data, leading to privacy violations and concerns regarding consent and data ownership.

Dependence on Data Quality

The performance of NLP models is heavily reliant on the quality of the data used for training. Poorly curated datasets can lead to inaccurate models, highlighting the importance of data governance and validation in the development process.

Influence and Impact

Natural Language Processing has profoundly influenced various domains, shaping the future of human-computer interaction and impacting societal communication. Its reach extends into multiple sectors, driving innovation and efficiency.

Improved Human-Computer Interaction

NLP enhances the ways in which individuals interact with computers, reducing reliance on specialized knowledge to operate technology. This democratization of technology allows for broader accessibility and usability of software applications.

Enhanced Communication

By enabling more efficient communication between individuals and organizations, NLP systems have the potential to bridge language barriers and improve collaboration in an increasingly globalized world. These tools assist in overcoming linguistic differences, facilitating smoother interactions.

Innovations in Business Intelligence

NLP's ability to analyze text data offers businesses valuable insights into customer preferences and market trends, driving informed decision-making. Companies leverage sentiment analysis and text mining to develop competitive strategies and enhance their offerings.

Advancements in Research

NLP contributes to advancements in various research fields by allowing scholars and scientists to sift through vast corpuses of literature and data. This capability accelerates knowledge discovery and promotes interdisciplinary research.

References

@@ Line 1: / Line 1: @@
-= Natural Language Processing =
+== Natural Language Processing ==
+Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables computers to understand, interpret, and manipulate human language. It is an interdisciplinary domain that integrates concepts from linguistics, computer science, and cognitive psychology. The goal of NLP is to facilitate interaction between humans and machines through natural language, allowing for more intuitive and efficient communication.
 == Introduction ==
-'''Natural Language Processing''' (NLP) is a interdisciplinary field that focuses on the interaction between computers and human (natural) languages. It encompasses the development of algorithms and models that enable machines to understand, interpret, and generate human language in a way that is both valuable and meaningful. NLP draws on insights and techniques from various fields such as linguistics, computer science, artificial intelligence, and cognitive psychology. The ultimate goal of NLP is to create systems that can communicate with humans in a natural and intuitive manner.
+NLP encompasses a range of techniques aimed at processing and analyzing large amounts of natural language data. It includes tasks such as text analysis, sentiment analysis, speech recognition, language translation, and chatbot functionality. As the volume of text and spoken data generated worldwide increases, the importance and applications of NLP have grown significantly, influencing industries ranging from customer service to healthcare.
+Natural Language Processing is crucial for developing applications that require a deep understanding of human language. It combines several areas of computer science, linguistics, and artificial intelligence with the objective of enabling machines to derive meaning from human language in a way that is similar to how people do.
 == History ==
-The field of NLP has evolved significantly since its inception in the mid-20th century. The roots of NLP can be traced back to the 1950s, when researchers began to develop early computational linguistics models. One of the first instances of machine translation was the Georgetown-IBM experiment in 1954, which translated sentences from Russian to English using symbolic rules.
-In the 1960s and 1970s, developments in linguistics and the introduction of formal grammar models such as Chomsky's generative grammar influenced NLP research. The introduction of the first natural language understanding systems showcased the capabilities of machines to parse and interpret structured language inputs.
+The history of Natural Language Processing can be traced back to the early days of computing in the 1950s. Researchers began exploring the potential of machines to understand and generate human language. One of the earliest attempts was the development of the first machine translation systems, which aimed to automatically translate text from one language to another. The initial enthusiasm was met with challenges due to the complexities and ambiguities inherent in human language.
-By the 1980s and 1990s, significant advances in statistical methods transformed NLP. Researchers began to employ probabilistic models for tasks like part-of-speech tagging and parsing. The growing availability of large corpora of written text spurred data-driven approaches, leading to the development of techniques such as Hidden Markov Models (HMMs) and Maximum Entropy models.
+In the 1960s and 1970s, efforts in NLP advanced with the development of more sophisticated algorithms and the emergence of syntax-based approaches, such as formal grammars. Significant progress was made with the introduction of rule-based systems that applied linguistic rules to parse and generate language. However, these systems often struggled with the richness of natural language, leading to a wave of skepticism about the feasibility of NLP.
-The advent of machine learning, particularly in the 2000s, catalyzed further progress in NLP. Techniques such as Support Vector Machines (SVMs) and neural networks became prominent as researchers sought to improve the accuracy and efficiency of natural language understanding systems. The introduction of deep learning in the 2010s revolutionized the field, with architectures like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) paving the way for advancements in tasks such as language modeling and text generation.
+The 1980s and 1990s saw a shift towards statistical methods in NLP, encouraged by the increasing availability of large corpora of text and the advancement of computational power. Researchers began using probabilistic models and machine learning techniques to analyze language data, resulting in more effective solutions for tasks such as part-of-speech tagging and named entity recognition.
-More recently, models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) have set new benchmarks in various NLP tasks, reflecting the significant impact of transformer architectures on the field.
+The early 21st century marked a significant turning point for NLP with the advent of deep learning. The development of neural networks enabled the creation of more complex models that could learn from vast amounts of textual data, leading to remarkable improvements in various NLP applications. Notable developments include recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and Transformer architectures, which have become foundational for modern NLP techniques.
 == Design and Architecture ==
-NLP systems are typically designed around several core components that work collaboratively to process and analyze human language. The design and architecture of these systems can vary significantly depending on the specific application, but typically include the following stages:
-=== Data Collection and Preprocessing ===
+The architecture of Natural Language Processing systems is generally based on a pipeline model that consists of several key components and stages. Each stage focuses on a specific aspect of language processing, incorporating various techniques from linguistics and machine learning.
-Data collection involves gathering large corpora of text, which can be sourced from books, websites, social media, and other domains. This raw data must go through preprocessing steps to clean and structure it for analysis. Preprocessing may involve:
-* Tokenization: Dividing text into words, phrases, or symbols.
+=== Preprocessing ===
-* Stopword Removal: Eliminating common words (like "the", "and") that may not contribute significant meaning to the text.
-* Lemmatization/Stemming: Reducing words to their base or root form to standardize variations.
+The first step in many NLP tasks involves preprocessing the text data. This stage typically includes:
-* Parsing: Analyzing the grammatical structure of sentences.
+* Tokenization: The process of dividing text into individual words or tokens.
+* Normalization: This includes converting all text to lowercase and removing punctuation.
+* Stop-word Removal: The elimination of common words (such as "the", "and", "is") that may not contribute significant meaning.
+* Stemming and Lemmatization: Techniques aimed at reducing words to their base or root form, enhancing the analysis and comparison of similar terms.
 === Feature Extraction ===
-Once the data is preprocessed, relevant features are extracted for machine learning models. Traditional methods include bag-of-words and term frequency-inverse document frequency (TF-IDF). More advanced approaches utilize word embeddings, where words are mathematically represented as vectors in high-dimensional space (e.g., Word2Vec, GloVe).
-=== Model Selection ===
+After preprocessing, the next stage involves feature extraction, where the system converts text into a numerical format that machine learning algorithms can process. This process commonly employs methods such as:
-Various models can be employed for specific NLP tasks. These may include:
+* Bag-of-Words Model: Represents text data as an unordered collection of words, neglecting grammar and order while capturing word frequency.
-* Rule-Based Models: Utilizing predefined linguistic rules.
+* Term Frequency-Inverse Document Frequency (TF-IDF): A statistical measure that evaluates the importance of a word in a document relative to a corpus.
-* Statistical Models: Relying on statistical inference to analyze patterns in language.
+* Word Embeddings: Techniques such as Word2Vec and GloVe represent words in a continuous vector space, capturing semantic relationships between words.
-* Machine Learning Models: Incorporating supervised, unsupervised, or semi-supervised learning techniques.
-* Neural Networks: Leveraging complex architectures to capture intricate relationships in data.
+=== Models and Algorithms ===
-=== Evaluation and Feedback ===
+At the core of NLP are several key models and algorithms that enable the processing and understanding of language. These include:
-Evaluation of NLP systems is critical to determine their effectiveness. Metrics such as precision, recall, F1-score, and accuracy are standard in measuring model performance on tasks like classification, translation, or sentiment analysis. Feedback loops help fine-tune models based on their performance.
+* N-grams: Probabilistic models that take into account sequences of ‘n’ words to provide context and predict likely outcomes in language.
+* Hidden Markov Models (HMMs): Particularly used for tasks like part-of-speech tagging, where the system makes predictions about the current state based on previous states.
+* Neural Networks: Deep learning models, especially those based on the Transformer architecture, such as BERT and GPT, have revolutionized NLP by allowing for context-aware language understanding and generation.
+=== Output Generation ===
+Finally, the output generation stage produces the desired results from the processed data. This can involve:
+* Text Classification: Assigning categories to text, such as spam detection or sentiment analysis.
+* Language Generation: Creating new text based on the learned patterns from the input data, found in applications like chatbots and summarization programs.
+* Translation: Automatically translating text from one language to another with the help of models that understand nuance and context.
 == Usage and Implementation ==
-NLP has found applications across various domains and industries, each leveraging its capabilities to derive insights from natural language data. Common use cases include:
-=== Text Classification ===
+Natural Language Processing has been widely adopted across various industries, demonstrating its versatility and applicability in real-world scenarios. Some key areas of implementation include:
-Text classification involves categorizing text into predefined categories. Applications include spam detection in emails, sentiment analysis in social media, and topic labeling in news articles.
+=== Information Retrieval ===
+NLP techniques facilitate the development of search engines and information retrieval systems that allow users to query vast amounts of textual data efficiently. By understanding user intent and context, these systems provide more relevant search results.
+=== Sentiment Analysis ===
+Companies utilize sentiment analysis to gauge public opinion about products, services, or events by analyzing customer reviews, social media posts, and online discussions. This analysis helps organizations make data-driven decisions and enhance customer satisfaction.
+=== Chatbots and Virtual Assistants ===
+NLP powers chatbots and virtual assistants, enabling them to understand and respond to user queries in natural language. These applications enhance customer service by providing instant responses and support, often used in sectors like e-commerce, banking, and healthcare.
 === Machine Translation ===
-Machine translation seeks to automatically translate text from one language to another. Systems like Google Translate utilize complex algorithms to provide real-time translations, enhancing cross-lingual communication.
-=== Chatbots and Virtual Assistants ===
+NLP allows for the development of machine translation systems that automatically translate text from one language to another, increasing accessibility to information across linguistic barriers. Advances in this area have improved the accuracy and fluency of translations, making them suitable for practical use.
-Chatbots and virtual assistants (e.g., Siri, Alexa) employ NLP to understand user queries and provide relevant responses. These systems leverage dialogue management and intent recognition techniques.
-=== Information Extraction ===
+=== Speech Recognition ===
-Information extraction systems identify and extract structured information from unstructured data sources, including named entity recognition, relationship extraction, and event detection.
-=== Text Generation ===
+Applications such as voice-activated assistants rely on NLP for speech recognition, which converts spoken language into text. This technology finds utility in various domains, including automotive systems, smart home devices, and customer service.
-NLP can also generate human-like text based on prompts or input data. Applications include automated content generation, dialogue systems, and creative writing.
 == Real-world Examples ==
-Real-world applications of NLP showcase the breadth and impact of this field in diverse sectors:
-=== Healthcare ===
+Specific implementations of Natural Language Processing in the real world demonstrate its capabilities and efficiency.
-NLP is increasingly being used to process medical literature and clinical notes, enabling healthcare professionals to derive insights from unstructured data. Applications include extracting information from patient records and aiding in diagnostic processes.
+=== Google Translate ===
+One of the most widely known applications of NLP is Google Translate, which employs deep learning techniques to provide real-time translation between multiple languages. By utilizing vast amounts of multilingual data, Google Translate continuously improves its accuracy and fluency.
-=== E-commerce ===
+=== Amazon Alexa ===
-E-commerce platforms employ NLP to enhance customer experience through recommendation systems, personalized content delivery, and sentiment analysis of customer reviews.
-=== Finance ===
+Amazon's Alexa is an example of an NLP-powered conversational agent that allows users to interact with devices using natural language. The system uses speech recognition and understanding algorithms to interpret user commands and respond accordingly.
-In finance, NLP algorithms analyze news articles, social media sentiments, and financial reports to predict market movements and gauge public sentiment regarding companies.
-=== Legal Sector ===
+=== IBM Watson ===
-Law firms utilize NLP for legal document review, contract analysis, and case prediction, transforming how legal professionals engage with voluminous paperwork and streamline processes.
+IBM Watson is a cognitive computing platform that leverages NLP to extract insights from unstructured data. Its applications range from healthcare diagnostics to customer support, enabling businesses to make informed decisions based on analyzed data.
+=== Grammarly ===
+Grammarly employs NLP to provide real-time writing assistance and grammar checking. By analyzing text for errors and suggesting improvements, Grammarly enhances writing quality and aids users in achieving effective communication.
 == Criticism and Controversies ==
-Despite its successes, NLP faces several criticisms and controversies that highlight challenges in the field.
-=== Bias and Fairness ===
+While Natural Language Processing has made significant strides, it is not without its criticisms and challenges. Some of the key areas of concern include:
-NLP models, especially those trained on uneven datasets, can inadvertently inherit biases present in the text. This raises ethical concerns about fairness and inclusivity, particularly when applications impact marginalized communities.
-=== Data Privacy ===
+=== Bias in AI ===
-The use of large-scale text corpora often raises questions regarding data privacy and user consent. Ensuring compliance with privacy regulations, like GDPR, while still harnessing the power of NLP remains a significant challenge.
-=== Misinterpretation and Errors ===
+NLP systems can inadvertently perpetuate biases present in the training data, leading to unjust outcomes in language generation and classification tasks. This issue raises ethical concerns about fairness, accountability, and transparency in AI-driven applications.
-NLP systems can struggle with context, leading to potential misinterpretations or erroneous outputs. This is especially problematic in sensitive areas such as medical advice or legal interpretations.
-=== Overreliance on Automation ===
+=== Misinterpretation of Context ===
-While NLP aids in enhancing efficiency, overreliance on automated systems may lead to diminished human oversight and critical thinking, posing risks in decision-making processes.
+Despite advancements in NLP technology, systems may still struggle to understand context, sarcasm, or nuances in human language. As a result, applications such as chatbots or virtual assistants may generate responses that are contextually inappropriate or misleading.
+=== Security and Privacy Concerns ===
+The collection and processing of large amounts of text data pose privacy risks. Models trained on sensitive information may inadvertently reveal personal data, leading to privacy violations and concerns regarding consent and data ownership.
+=== Dependence on Data Quality ===
+The performance of NLP models is heavily reliant on the quality of the data used for training. Poorly curated datasets can lead to inaccurate models, highlighting the importance of data governance and validation in the development process.
 == Influence and Impact ==
-The influence of NLP extends beyond technology, shaping interactions between humans and machines. Its impact encompasses:
+Natural Language Processing has profoundly influenced various domains, shaping the future of human-computer interaction and impacting societal communication. Its reach extends into multiple sectors, driving innovation and efficiency.
 === Improved Human-Computer Interaction ===
-NLP has profoundly improved the way humans communicate with machines, fostering more intuitive interfaces that enhance user experience. Voice-activated assistants and chatbots exemplify this transformation.
-=== Advancements in AI ===
+NLP enhances the ways in which individuals interact with computers, reducing reliance on specialized knowledge to operate technology. This democratization of technology allows for broader accessibility and usability of software applications.
-NLP continues to drive advancements in artificial intelligence, contributing to growing interest and investment in machine learning and deep learning research. This synergy accelerates innovation across various fields.
+=== Enhanced Communication ===
+By enabling more efficient communication between individuals and organizations, NLP systems have the potential to bridge language barriers and improve collaboration in an increasingly globalized world. These tools assist in overcoming linguistic differences, facilitating smoother interactions.
-=== Democratization of Content Creation ===
+=== Innovations in Business Intelligence ===
-NLP tools enable individuals and organizations to automatically generate content, democratizing access to creative processes. Applications in journalism, marketing, and entertainment showcase this potential.
-=== Knowledge Discovery ===
+NLP's ability to analyze text data offers businesses valuable insights into customer preferences and market trends, driving informed decision-making. Companies leverage sentiment analysis and text mining to develop competitive strategies and enhance their offerings.
-As organizations increasingly rely on data-driven decision-making, NLP aids in parsing vast amounts of unstructured information, unearthing insights that inform strategic initiatives.
+=== Advancements in Research ===
+NLP contributes to advancements in various research fields by allowing scholars and scientists to sift through vast corpuses of literature and data. This capability accelerates knowledge discovery and promotes interdisciplinary research.
 == See also ==
+* [[Artificial Intelligence]]
+* [[Machine Learning]]
 * [[Computational Linguistics]]
-* [[Machine Learning]]
-* [[Artificial Intelligence]]
-* [[Deep Learning]]
 * [[Speech Recognition]]
+* [[Machine Translation]]
 * [[Text Mining]]
+* [[Sentiment Analysis]]
 == References ==
-* [[https://www.ibm.com/cloud/learn/natural-language-processing]]
+* [https://www.ibm.com/watson Natural Language Processing | IBM Watson]
-* [[https://aws.amazon.com/machine-learning/natural-language-processing/]]
+* [https://www.amazon.com/alexa Alexa - Voice Service | Amazon]
-* [[https://en.wikipedia.org/wiki/Natural_language_processing]]
+* [https://translate.google.com Google Translate]
-* [[https://www.microsoft.com/en-us/research/research-area/natural-language-processing/]]
+* [https://grammarly.com Grammarly: AI-Powered Writing Assistant]
-* [[https://towardsdatascience.com/natural-language-processing-in-python-2021-3c881308c7cd]]
+* [https://towardsdatascience.com/the-history-of-natural-language-processing-in-one-article-a9b8fbb3e3fa The History of Natural Language Processing in One Article]
+* [https://www.oreilly.com/library/view/hands-on-natural-language/9781492039781/ Hands-On Natural Language Processing with Python - O'Reilly Media]
+* [https://machinelearningmastery.com/a-gentle-introduction-to-natural-language-processing/ A Gentle Introduction to Natural Language Processing]
-[[Category:Natural language processing]]
 [[Category:Artificial intelligence]]
 [[Category:Computer science]]
+[[Category:Natural language processing]]