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Natural Language Processing - Coggle Diagram
Natural Language Processing
Definition of NPL
Natural language processing (NLP) is the ability of a computer program to understand human language as it's spoken and written -- referred to as natural language. It's a component of artificial intelligence (AI).
NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in numerous fields, including medical research, search engines and business intelligence.
Rule-based system.
This system uses carefully designed linguistic rules. This approach was used early in the development of natural language processing and is still used.
Machine learning-based system
. Machine learning algorithms use statistical methods.
Benefits of natural language processing
.
Offers improved accuracy and efficiency of documentation.
Enables an organization to use chatbots for customer support.
Provides an organization with the ability to automatically make a readable summary of a larger, more complex original text.
Lets organizations analyze structured and unstructured data.
Enables personal assistants such as Alexa to understand the spoken word.
Makes it easier for organizations to perform sentiment analysis.
Formal v natural language
Natural languages
are the languages that people speak, such as English, Spanish, and French. They were not designed by people (although people try to impose some order on them); they evolved naturally.
Formal languages
are languages that are designed by people for specific applications. For example, the notation that mathematicians use is a formal language that is particularly good at denoting relationships among numbers and symbols.
Web Links
https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP
https://runestone.academy/ns/books/published/thinkcspy/GeneralIntro/FormalandNaturalLanguages.html
https://www.javatpoint.com/advantages-and-disadvantages-of-machine-learning
https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP#:~:text=NLP%20was%20largely%20rules%2Dbased,sentences%20from%20Russian%20to%20English
.
https://www.geeksforgeeks.org/understanding-semantic-analysis-nlp/
Machine learning :
Machine Learning is significant because it gives corporates, businesses, and enterprises to observe trends, business operation patterns, and customer behavior and fosters the development of new products.
Advantages
Wide Range of Applicability
Enhanced Experience in Online Shopping and Quality Education
Automation
Scope of Improvement
Disadvantages
Social Changes
High Error Chances
Results Interpretations
Time and Resources
Data Acquisition
Applications
.
Sentiment Analysis
Text Classification
Chatbots & Virtual Assistants
Text Extraction
Machine Translation
Text Summarization
Market Intelligence
Auto-Correct
Intent Classification
Urgency Detection
Speech Recognition
Lexicon
In general, lexicons play an important role in many NLP applications by providing a rich source of linguistic information that can be used to improve the accuracy and efficiency of text analysis and processing.
Idioms
ex: Dark Horse, Get cold feet , Lose face , Throw in the towel
Neologisms
ex: Unfriend , Retweet , Bromance
Non-standard English
An informal version of english
N-gram
An n-gram is a collection of n successive items in a text document that may include words, numbers, symbols, and punctuation. N-gram models are useful in many text analytics applications where sequences of words are relevant, such as in sentiment analysis, text classification, and text generation.
Fields an Tools
Big data (Ex: Hadoop, Spark .....)
Deep learning (FFNN,RNN,CNN...)
Machine Learning Libraries (Ex: Scikit-learn)
Machine Learning Frameworks (Ex: Tensorflow,keras,pytouch...)
History
1950s
.Natural language processing has its roots in this decade, when Alan Turing developed the Turing Test to determine whether or not a computer is truly intelligent.
1950s-1990s.
NLP was largely rules-based, using handcrafted rules developed by linguists to determine how computers would process language.
1990s.
The top-down, language-first approach to natural language processing was replaced with a more statistical approach because advancements in computing made this a more efficient way of developing NLP technology.
2000-2020s.
Natural language processing saw dramatic growth in popularity as a term.
Syntax
Syntax analysis, also known as parsing, is the process of analyzing a string of symbols, either in natural language or in a computer language, according to the rules of formal grammar.
Semantic analysis
Semantic analysis is a crucial component of natural language processing (NLP) that concentrates on understanding the meaning, interpretation, and relationships between words, phrases.
Lexical Semantic Analysis:
Lexical Semantic Analysis involves understanding the meaning of each word of the text individually. It basically refers to fetching the dictionary meaning that a word in the text is deputed to carry.
Compositional Semantics Analysis:
Although knowing the meaning of each word of the text is essential, it is not sufficient to completely understand the meaning of the text.
