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NATURAL
LANGUAGE
PROCESSING
ABSTRACT
What do we want machines to learn?
Prediction or labelling Reasoning
Understanding Language generation
INTRODUCTION
NLP is a way for computers to analyze, understand,
and derive meaning from human language in a smart
and useful way.
By utilizing NLP, developers can organize and
structure knowledge to perform tasks such as
automatic summarization, translation, named entity
recognition, relationship extraction, sentiment
analysis, speech recognition, and topic segmentation.
INTRODUCTION
NLP algorithms are typically based on machine learning algorithms. Instead of hand-
coding large sets of rules, NLP can rely on machine learning to automatically learn
these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a
collection of sentences), and making a statistical inference. In general, the more data
analyzed, the more accurate the model will be.
o Summarize blocks of text using Summarizer to extract the most important and central ideas whileignoring irrelevant
information.
o Create a chatbot using Parsey McParseface, a language parsing deep learning model made by Google that uses Point-
of-Speech tagging.
o Identify the type of entity extracted, such as it being a person, place, or organization using Named Entity
Recognition.
o Use Sentiment Analysis to identify the sentiment of a string of text, from very negative to neutralTo very positive.
o Reduce words to their root, or stem, using PorterStemmer, or break up text into tokens using Tokenizer.
FURTHER DAILY EXAMPLES
• Language translation applications such as Google Translate
• Word Processors such as Microsoft Word and Grammarly that
employ NLP to check grammatical accuracy of texts.
• Interactive Voice Response (IVR) applications used in call
centers to respond to certain users’ requests.
• Personal assistant applications such as OK Google, Siri,
Cortana, and Alexa.
HOW DOES NATURAL LANGUAGE
PROCESSING WORKS?
 NLP entails applying algorithms to identify and extract the natural language
rules such that the unstructured language data is converted into a form that
computers can understand.
 When the text has been provided, the computer will utilize algorithms to
extract meaning associated with every sentence and collect the essential data
from them.
 Sometimes, the computer may fail to understand the meaning of a sentence
well, leading to obscure results.
 For example, a humorous incident occurred in the 1950s during the translation
of some words between the English and the Russian languages.
Here is the biblical sentence that required translation:
“The spirit is willing, but the flesh is weak.”
Here is the result when the sentence was translated to Russian and back to English:
“The vodka is good, but the meat is rotten.”
WHAT ARE THE TECHNIQUES
USED IN NLP?
Syntactic analysis and Semantic analysis are the main techniques used
to complete Natural Language Processing tasks.
1. Syntax: Syntax refers to the arrangement of words in a sentence such that they
make grammatical sense. In NLP, syntactic analysis is used to assess how the natural
language aligns with the grammatical rules. Computer algorithms are used to apply
grammatical rules to a group of words and derive meaning from them. Here are some
syntax techniques that can be used:
Morphological segmentation: It involves dividing words into individual units called morphemes.
Word segmentation: It involves dividing a large piece of continuous text into distinct units.
Part-of-speech tagging: It involves identifying the part of speech for every word.
Parsing: It involves undertaking grammatical analysis for the provided sentence.
Sentence breaking: It involves placing sentence boundaries on a large piece of text.
Stemming: It involves cutting the inflected words to their root form.
WHAT ARE THE TECHNIQUES
USED IN NLP?
2. Semantics: Semantics refers to the meaning that is conveyed by a
text. Semantic analysis is one of the difficult aspects of Natural Language
Processing that has not been fully resolved yet.
It involves applying computer algorithms to understand the meaning and
interpretation of words and how sentences are structured.
Here are some techniques in semantic analysis:
Named entity recognition (NER): It involves determining the parts of a text
that can be identified and categorized into preset groups. Examples of such
groups include names of people and names of places.
Word sense disambiguation: It involves giving meaning to a word based on
the context.
Natural language generation: It involves using databases to derive semantic
intentions and convert them into human language.
CONCLUSION AND FUTURE
OF NLP
• Human level or human readable natural language processingis an AI-
complete problem
• It is equivalent to solving the central artificial intelligence problem and
making computers as intelligent as people
• Make computers as they can solve problems like humans and think like
humans as well as perform activities that humans cant perform and
making it more efficient than humans.
• NLP's future is closely linked to the growth of Artificialintelligence
• As natural language understanding or readability improves, computers
or machines or devices will be able to learn from the information online
and apply what they learned in the real world
• Combined with natural language generation, computers will become
more and more capable of receiving and giving useful and resourceful
information or data

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Natural Language Processing

  • 2. ABSTRACT What do we want machines to learn? Prediction or labelling Reasoning Understanding Language generation
  • 3. INTRODUCTION NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
  • 4. INTRODUCTION NLP algorithms are typically based on machine learning algorithms. Instead of hand- coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statistical inference. In general, the more data analyzed, the more accurate the model will be. o Summarize blocks of text using Summarizer to extract the most important and central ideas whileignoring irrelevant information. o Create a chatbot using Parsey McParseface, a language parsing deep learning model made by Google that uses Point- of-Speech tagging. o Identify the type of entity extracted, such as it being a person, place, or organization using Named Entity Recognition. o Use Sentiment Analysis to identify the sentiment of a string of text, from very negative to neutralTo very positive. o Reduce words to their root, or stem, using PorterStemmer, or break up text into tokens using Tokenizer.
  • 5. FURTHER DAILY EXAMPLES • Language translation applications such as Google Translate • Word Processors such as Microsoft Word and Grammarly that employ NLP to check grammatical accuracy of texts. • Interactive Voice Response (IVR) applications used in call centers to respond to certain users’ requests. • Personal assistant applications such as OK Google, Siri, Cortana, and Alexa.
  • 6. HOW DOES NATURAL LANGUAGE PROCESSING WORKS?  NLP entails applying algorithms to identify and extract the natural language rules such that the unstructured language data is converted into a form that computers can understand.  When the text has been provided, the computer will utilize algorithms to extract meaning associated with every sentence and collect the essential data from them.  Sometimes, the computer may fail to understand the meaning of a sentence well, leading to obscure results.  For example, a humorous incident occurred in the 1950s during the translation of some words between the English and the Russian languages. Here is the biblical sentence that required translation: “The spirit is willing, but the flesh is weak.” Here is the result when the sentence was translated to Russian and back to English: “The vodka is good, but the meat is rotten.”
  • 7. WHAT ARE THE TECHNIQUES USED IN NLP? Syntactic analysis and Semantic analysis are the main techniques used to complete Natural Language Processing tasks. 1. Syntax: Syntax refers to the arrangement of words in a sentence such that they make grammatical sense. In NLP, syntactic analysis is used to assess how the natural language aligns with the grammatical rules. Computer algorithms are used to apply grammatical rules to a group of words and derive meaning from them. Here are some syntax techniques that can be used: Morphological segmentation: It involves dividing words into individual units called morphemes. Word segmentation: It involves dividing a large piece of continuous text into distinct units. Part-of-speech tagging: It involves identifying the part of speech for every word. Parsing: It involves undertaking grammatical analysis for the provided sentence. Sentence breaking: It involves placing sentence boundaries on a large piece of text. Stemming: It involves cutting the inflected words to their root form.
  • 8. WHAT ARE THE TECHNIQUES USED IN NLP? 2. Semantics: Semantics refers to the meaning that is conveyed by a text. Semantic analysis is one of the difficult aspects of Natural Language Processing that has not been fully resolved yet. It involves applying computer algorithms to understand the meaning and interpretation of words and how sentences are structured. Here are some techniques in semantic analysis: Named entity recognition (NER): It involves determining the parts of a text that can be identified and categorized into preset groups. Examples of such groups include names of people and names of places. Word sense disambiguation: It involves giving meaning to a word based on the context. Natural language generation: It involves using databases to derive semantic intentions and convert them into human language.
  • 9.
  • 10. CONCLUSION AND FUTURE OF NLP • Human level or human readable natural language processingis an AI- complete problem • It is equivalent to solving the central artificial intelligence problem and making computers as intelligent as people • Make computers as they can solve problems like humans and think like humans as well as perform activities that humans cant perform and making it more efficient than humans. • NLP's future is closely linked to the growth of Artificialintelligence • As natural language understanding or readability improves, computers or machines or devices will be able to learn from the information online and apply what they learned in the real world • Combined with natural language generation, computers will become more and more capable of receiving and giving useful and resourceful information or data