SlideShare a Scribd company logo

Natural Language Understanding System Explained

•
•
I
inscit2006

The document describes the development of a natural language understanding system with 6 modules for morphological analysis, synonym matching, syntax analysis, semantic analysis, and knowledge base interaction to understand commands in English sentences and execute the corresponding shell command. It discusses the methodology used in building the modules and evaluates the system's performance on 50 test sentences, achieving a 94% precision in generating the correct responses.

TechnologyEducation
1 of 42
An Intuitive Natural Language Understanding System Vanitadevi Patil
Contents ,[object Object],[object Object],[object Object],[object Object]
What is NLP? ,[object Object]
Components of NLP ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object]
Knowledge of Language ,[object Object],[object Object],[object Object],[object Object]
Methodology ,[object Object],[object Object],[object Object],[object Object]
Modules in building up NLU ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Input Preprocessor ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Input Preprocessor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Input Preprocessor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Morphological Analysis ,[object Object],[object Object],[object Object],[object Object]
Morphological Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Synonym Matching ,[object Object],[object Object],[object Object]
Synonym Matching ,[object Object],[object Object],[object Object]
Syntax Analysis ,[object Object],[object Object]
Context-free Grammars ,[object Object],[object Object],[object Object],[object Object],[object Object]
A simple CFG ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Different sentence structures ,[object Object],[object Object],[object Object],[object Object],[object Object]
Declarative and Imperative sentences ,[object Object],[object Object],[object Object],[object Object]
Yes-no-question and wh-question sentences ,[object Object],[object Object],[object Object],[object Object]
Chart parser ,[object Object],[object Object],[object Object]
Chart parser ,[object Object],[object Object],[object Object],[object Object],[object Object]
Chart parser ,[object Object],[object Object]
Chart parser ,[object Object],[object Object]
Word classes ,[object Object],[object Object],[object Object],[object Object]
Word classes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Parse tree as a list ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantic analysis ,[object Object],[object Object]
Knowledge Base ,[object Object],[object Object]
Knowledge Base ,[object Object],[object Object]
Knowledge Base ,[object Object],[object Object],[object Object]
Priority of chief predicates ,[object Object],[object Object]
Priority of chief predicates ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predicates, Operations and Constants ,[object Object],[object Object],[object Object]
Predicates, Operations and Constants ,[object Object],[object Object]
Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object],[object Object]
The response analysis of four different types of structures 00 12 12 Wh-question 01 07 08 Yes-No Question 02 14 16 Imperative 00 14 14 Declarative Number of wrong responses Number of Correct responses Number of Sentences Sentence Type
Results ,[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object],[object Object]
[object Object]

Recommended

Improvement in Quality of Speech associated with Braille codes - A Review
Improvement in Quality of Speech associated with Braille codes - A ReviewImprovement in Quality of Speech associated with Braille codes - A Review
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
 
Taking into account communities of practice’s specific vocabularies in inform...
Taking into account communities of practice’s specific vocabularies in inform...Taking into account communities of practice’s specific vocabularies in inform...
Taking into account communities of practice’s specific vocabularies in inform...inscit2006
 
14. Michael Oakes (UoW) Natural Language Processing for Translation
14. Michael Oakes (UoW) Natural Language Processing for Translation14. Michael Oakes (UoW) Natural Language Processing for Translation
14. Michael Oakes (UoW) Natural Language Processing for TranslationRIILP
 
Word Segmentation and Lexical Normalization for Unsegmented Languages
Word Segmentation and Lexical Normalization for Unsegmented LanguagesWord Segmentation and Lexical Normalization for Unsegmented Languages
Word Segmentation and Lexical Normalization for Unsegmented Languageshs0041
 
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...Editor IJARCET
 
I1 geetha3 revathi
I1 geetha3 revathiI1 geetha3 revathi
I1 geetha3 revathiJasline Presilda
 
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...cscpconf
 
Phonetic Recognition In Words For Persian Text To Speech Systems
Phonetic Recognition In Words For Persian Text To Speech SystemsPhonetic Recognition In Words For Persian Text To Speech Systems
Phonetic Recognition In Words For Persian Text To Speech Systemspaperpublications3
 

Recommended

Improvement in Quality of Speech associated with Braille codes - A Review
Improvement in Quality of Speech associated with Braille codes - A ReviewImprovement in Quality of Speech associated with Braille codes - A Review
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
 
Taking into account communities of practice’s specific vocabularies in inform...
Taking into account communities of practice’s specific vocabularies in inform...Taking into account communities of practice’s specific vocabularies in inform...
Taking into account communities of practice’s specific vocabularies in inform...inscit2006
 
14. Michael Oakes (UoW) Natural Language Processing for Translation
14. Michael Oakes (UoW) Natural Language Processing for Translation14. Michael Oakes (UoW) Natural Language Processing for Translation
14. Michael Oakes (UoW) Natural Language Processing for TranslationRIILP
 
Word Segmentation and Lexical Normalization for Unsegmented Languages
Word Segmentation and Lexical Normalization for Unsegmented LanguagesWord Segmentation and Lexical Normalization for Unsegmented Languages
Word Segmentation and Lexical Normalization for Unsegmented Languageshs0041
 
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...
A Combined Approach to Part-of-Speech Tagging Using Features Extraction and H...Editor IJARCET
 
I1 geetha3 revathi
I1 geetha3 revathiI1 geetha3 revathi
I1 geetha3 revathiJasline Presilda
 
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...cscpconf
 
Phonetic Recognition In Words For Persian Text To Speech Systems
Phonetic Recognition In Words For Persian Text To Speech SystemsPhonetic Recognition In Words For Persian Text To Speech Systems
Phonetic Recognition In Words For Persian Text To Speech Systemspaperpublications3
 
Segmentation Words for Speech Synthesis in Persian Language Based On Silence
Segmentation Words for Speech Synthesis in Persian Language Based On SilenceSegmentation Words for Speech Synthesis in Persian Language Based On Silence
Segmentation Words for Speech Synthesis in Persian Language Based On Silencepaperpublications3
 
13. Constantin Orasan (UoW) Natural Language Processing for Translation
13. Constantin Orasan (UoW) Natural Language Processing for Translation13. Constantin Orasan (UoW) Natural Language Processing for Translation
13. Constantin Orasan (UoW) Natural Language Processing for TranslationRIILP
 
Ijartes v1-i1-002
Ijartes v1-i1-002Ijartes v1-i1-002
Ijartes v1-i1-002IJARTES
 
Pedagogical applications of corpus data for English for General and Specific ...
Pedagogical applications of corpus data for English for General and Specific ...Pedagogical applications of corpus data for English for General and Specific ...
Pedagogical applications of corpus data for English for General and Specific ...Pascual Pérez-Paredes
 
FIRE2014_IIT-P
FIRE2014_IIT-PFIRE2014_IIT-P
FIRE2014_IIT-PShubham Kumar
 
A New Approach to Parts of Speech Tagging in Malayalam
A New Approach to Parts of Speech Tagging in MalayalamA New Approach to Parts of Speech Tagging in Malayalam
A New Approach to Parts of Speech Tagging in Malayalamijcsit
 
Automatic Key Term Extraction and Summarization from Spoken Course Lectures
Automatic Key Term Extraction and Summarization from Spoken Course LecturesAutomatic Key Term Extraction and Summarization from Spoken Course Lectures
Automatic Key Term Extraction and Summarization from Spoken Course LecturesYun-Nung (Vivian) Chen
 
Arabic MT Project
Arabic MT ProjectArabic MT Project
Arabic MT ProjectHind Abdulkhaleq
 
Ijarcet vol-3-issue-1-9-11
Ijarcet vol-3-issue-1-9-11Ijarcet vol-3-issue-1-9-11
Ijarcet vol-3-issue-1-9-11Dhabal Sethi
 
Automatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesAutomatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesYun-Nung (Vivian) Chen
 
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorDynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
 
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memoriesRIILP
 
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...kevig
 
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...iosrjce
 
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATION
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATIONA ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATION
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATIONkevig
 
Token classification using Bengali Tokenizer
Token classification using Bengali TokenizerToken classification using Bengali Tokenizer
Token classification using Bengali TokenizerJeet Das
 
SMT3
SMT3SMT3
SMT3khyati gupta
 
Named Entity Recognition using Hidden Markov Model (HMM)
Named Entity Recognition using Hidden Markov Model (HMM)Named Entity Recognition using Hidden Markov Model (HMM)
Named Entity Recognition using Hidden Markov Model (HMM)kevig
 
Text Tokenization
Text TokenizationText Tokenization
Text TokenizationBryan Warner
 
Text Mining Analytics 101
Text Mining Analytics 101Text Mining Analytics 101
Text Mining Analytics 101Manohar Swamynathan
 
Shallow parser for hindi language with an input from a transliterator
Shallow parser for hindi language with an input from a transliteratorShallow parser for hindi language with an input from a transliterator
Shallow parser for hindi language with an input from a transliteratorShashank Shisodia
 
Chinese Word Segmentation in MSR-NLP
Chinese Word Segmentation in MSR-NLPChinese Word Segmentation in MSR-NLP
Chinese Word Segmentation in MSR-NLPAndi Wu
 

More Related Content

What's hot

Segmentation Words for Speech Synthesis in Persian Language Based On Silence
Segmentation Words for Speech Synthesis in Persian Language Based On SilenceSegmentation Words for Speech Synthesis in Persian Language Based On Silence
Segmentation Words for Speech Synthesis in Persian Language Based On Silencepaperpublications3
 
13. Constantin Orasan (UoW) Natural Language Processing for Translation
13. Constantin Orasan (UoW) Natural Language Processing for Translation13. Constantin Orasan (UoW) Natural Language Processing for Translation
13. Constantin Orasan (UoW) Natural Language Processing for TranslationRIILP
 
Ijartes v1-i1-002
Ijartes v1-i1-002Ijartes v1-i1-002
Ijartes v1-i1-002IJARTES
 
Pedagogical applications of corpus data for English for General and Specific ...
Pedagogical applications of corpus data for English for General and Specific ...Pedagogical applications of corpus data for English for General and Specific ...
Pedagogical applications of corpus data for English for General and Specific ...Pascual Pérez-Paredes
 
A New Approach to Parts of Speech Tagging in Malayalam
A New Approach to Parts of Speech Tagging in MalayalamA New Approach to Parts of Speech Tagging in Malayalam
A New Approach to Parts of Speech Tagging in Malayalamijcsit
 
Automatic Key Term Extraction and Summarization from Spoken Course Lectures
Automatic Key Term Extraction and Summarization from Spoken Course LecturesAutomatic Key Term Extraction and Summarization from Spoken Course Lectures
Automatic Key Term Extraction and Summarization from Spoken Course LecturesYun-Nung (Vivian) Chen
 
Ijarcet vol-3-issue-1-9-11
Ijarcet vol-3-issue-1-9-11Ijarcet vol-3-issue-1-9-11
Ijarcet vol-3-issue-1-9-11Dhabal Sethi
 
Automatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesAutomatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesYun-Nung (Vivian) Chen
 
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorDynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
 
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memoriesRIILP
 
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...kevig
 
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...iosrjce
 
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATION
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATIONA ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATION
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATIONkevig
 
Token classification using Bengali Tokenizer
Token classification using Bengali TokenizerToken classification using Bengali Tokenizer
Token classification using Bengali TokenizerJeet Das
 
Named Entity Recognition using Hidden Markov Model (HMM)
Named Entity Recognition using Hidden Markov Model (HMM)Named Entity Recognition using Hidden Markov Model (HMM)
Named Entity Recognition using Hidden Markov Model (HMM)kevig
 

What's hot (20)

Segmentation Words for Speech Synthesis in Persian Language Based On Silence
Segmentation Words for Speech Synthesis in Persian Language Based On SilenceSegmentation Words for Speech Synthesis in Persian Language Based On Silence
Segmentation Words for Speech Synthesis in Persian Language Based On Silence
 
13. Constantin Orasan (UoW) Natural Language Processing for Translation
13. Constantin Orasan (UoW) Natural Language Processing for Translation13. Constantin Orasan (UoW) Natural Language Processing for Translation
13. Constantin Orasan (UoW) Natural Language Processing for Translation
 
Ijartes v1-i1-002
Ijartes v1-i1-002Ijartes v1-i1-002
Ijartes v1-i1-002
 
Pedagogical applications of corpus data for English for General and Specific ...
Pedagogical applications of corpus data for English for General and Specific ...Pedagogical applications of corpus data for English for General and Specific ...
Pedagogical applications of corpus data for English for General and Specific ...
 
FIRE2014_IIT-P
FIRE2014_IIT-PFIRE2014_IIT-P
FIRE2014_IIT-P
 
A New Approach to Parts of Speech Tagging in Malayalam
A New Approach to Parts of Speech Tagging in MalayalamA New Approach to Parts of Speech Tagging in Malayalam
A New Approach to Parts of Speech Tagging in Malayalam
 
Automatic Key Term Extraction and Summarization from Spoken Course Lectures
Automatic Key Term Extraction and Summarization from Spoken Course LecturesAutomatic Key Term Extraction and Summarization from Spoken Course Lectures
Automatic Key Term Extraction and Summarization from Spoken Course Lectures
 
Arabic MT Project
Arabic MT ProjectArabic MT Project
Arabic MT Project
 
Ijarcet vol-3-issue-1-9-11
Ijarcet vol-3-issue-1-9-11Ijarcet vol-3-issue-1-9-11
Ijarcet vol-3-issue-1-9-11
 
Automatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course LecturesAutomatic Key Term Extraction from Spoken Course Lectures
Automatic Key Term Extraction from Spoken Course Lectures
 
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorDynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
 
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
5. manuel arcedillo & juanjo arevalillo (hermes) translation memories
 
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
 
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...
Artificially Generatedof Concatenative Syllable based Text to Speech Synthesi...
 
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATION
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATIONA ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATION
A ROBUST THREE-STAGE HYBRID FRAMEWORK FOR ENGLISH TO BANGLA TRANSLITERATION
 
Token classification using Bengali Tokenizer
Token classification using Bengali TokenizerToken classification using Bengali Tokenizer
Token classification using Bengali Tokenizer
 
SMT3
SMT3SMT3
SMT3
 
Named Entity Recognition using Hidden Markov Model (HMM)
Named Entity Recognition using Hidden Markov Model (HMM)Named Entity Recognition using Hidden Markov Model (HMM)
Named Entity Recognition using Hidden Markov Model (HMM)
 
Text Tokenization
Text TokenizationText Tokenization
Text Tokenization
 
Text Mining Analytics 101
Text Mining Analytics 101Text Mining Analytics 101
Text Mining Analytics 101
 

Similar to Natural Language Understanding System Explained

Shallow parser for hindi language with an input from a transliterator
Shallow parser for hindi language with an input from a transliteratorShallow parser for hindi language with an input from a transliterator
Shallow parser for hindi language with an input from a transliteratorShashank Shisodia
 
Chinese Word Segmentation in MSR-NLP
Chinese Word Segmentation in MSR-NLPChinese Word Segmentation in MSR-NLP
Chinese Word Segmentation in MSR-NLPAndi Wu
 
Chapter 2 Text Operation.pdf
Chapter 2 Text Operation.pdfChapter 2 Text Operation.pdf
Chapter 2 Text Operation.pdfHabtamu100
 
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI) International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI) inventionjournals
 
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI)International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI)inventionjournals
 
Implementation Of Syntax Parser For English Language Using Grammar Rules
Implementation Of Syntax Parser For English Language Using Grammar RulesImplementation Of Syntax Parser For English Language Using Grammar Rules
Implementation Of Syntax Parser For English Language Using Grammar RulesIJERA Editor
 
NLP Deep Learning with Tensorflow
NLP Deep Learning with TensorflowNLP Deep Learning with Tensorflow
NLP Deep Learning with Tensorflowseungwoo kim
 
Syntax analyzer
Syntax analyzerSyntax analyzer
Syntax analyzerahmed51236
 
Understanding Natural Languange with Corpora-based Generation of Dependency G...
Understanding Natural Languange with Corpora-based Generation of Dependency G...Understanding Natural Languange with Corpora-based Generation of Dependency G...
Understanding Natural Languange with Corpora-based Generation of Dependency G...Edmond Lepedus
 
DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)
DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)
DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)ijnlc
 
CH 2.pptx
CH 2.pptxCH 2.pptx
CH 2.pptxObsa2
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language ProcessingMariana Soffer
 
Chapter 2: Text Operation in information stroage and retrieval
Chapter 2: Text Operation in information stroage and retrievalChapter 2: Text Operation in information stroage and retrieval
Chapter 2: Text Operation in information stroage and retrievalcaptainmactavish1996
 
Unit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmm
Unit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmmUnit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmm
Unit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmmDhruvKushwaha12
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
 

Similar to Natural Language Understanding System Explained (20)

Shallow parser for hindi language with an input from a transliterator
Shallow parser for hindi language with an input from a transliteratorShallow parser for hindi language with an input from a transliterator
Shallow parser for hindi language with an input from a transliterator
 
Chinese Word Segmentation in MSR-NLP
Chinese Word Segmentation in MSR-NLPChinese Word Segmentation in MSR-NLP
Chinese Word Segmentation in MSR-NLP
 
Parser
ParserParser
Parser
 
Chapter 2 Text Operation.pdf
Chapter 2 Text Operation.pdfChapter 2 Text Operation.pdf
Chapter 2 Text Operation.pdf
 
nlp (1).pptx
nlp (1).pptxnlp (1).pptx
nlp (1).pptx
 
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI) International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI)
 
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI)International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI)
 
Implementation Of Syntax Parser For English Language Using Grammar Rules
Implementation Of Syntax Parser For English Language Using Grammar RulesImplementation Of Syntax Parser For English Language Using Grammar Rules
Implementation Of Syntax Parser For English Language Using Grammar Rules
 
NLP Deep Learning with Tensorflow
NLP Deep Learning with TensorflowNLP Deep Learning with Tensorflow
NLP Deep Learning with Tensorflow
 
Syntax analyzer
Syntax analyzerSyntax analyzer
Syntax analyzer
 
Textmining
TextminingTextmining
Textmining
 
Understanding Natural Languange with Corpora-based Generation of Dependency G...
Understanding Natural Languange with Corpora-based Generation of Dependency G...Understanding Natural Languange with Corpora-based Generation of Dependency G...
Understanding Natural Languange with Corpora-based Generation of Dependency G...
 
DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)
DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)
DEVELOPMENT OF ARABIC NOUN PHRASE EXTRACTOR (ANPE)
 
CH 2.pptx
CH 2.pptxCH 2.pptx
CH 2.pptx
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
 
Chapter 2: Text Operation in information stroage and retrieval
Chapter 2: Text Operation in information stroage and retrievalChapter 2: Text Operation in information stroage and retrieval
Chapter 2: Text Operation in information stroage and retrieval
 
Unit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmm
Unit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmmUnit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmm
Unit-1 PPL PPTvvhvmmmmmmmmmmmmmmmmmmmmmm
 
NLP todo
NLP todoNLP todo
NLP todo
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
 
NLP
NLPNLP
NLP
 

More from inscit2006

Information Searcher-Provider Fit through Information Presentation and Visual...
Information Searcher-Provider Fit through Information Presentation and Visual...Information Searcher-Provider Fit through Information Presentation and Visual...
Information Searcher-Provider Fit through Information Presentation and Visual...inscit2006
 
Difference of application of fuzzy rough sets and probability random on targe...
Difference of application of fuzzy rough sets and probability random on targe...Difference of application of fuzzy rough sets and probability random on targe...
Difference of application of fuzzy rough sets and probability random on targe...inscit2006
 
The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...
The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...
The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...inscit2006
 
Weighted Naïve Bayes Model for Semi-Structured Document Categorization
Weighted Naïve Bayes Model for Semi-Structured Document CategorizationWeighted Naïve Bayes Model for Semi-Structured Document Categorization
Weighted Naïve Bayes Model for Semi-Structured Document Categorizationinscit2006
 
The role of education within the framework of information sciences and techno...
The role of education within the framework of information sciences and techno...The role of education within the framework of information sciences and techno...
The role of education within the framework of information sciences and techno...inscit2006
 
A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...
A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...
A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...inscit2006
 
A Comparative Study of RDBMs and OODBMs in Relation to Security of Data
A Comparative Study of RDBMs and OODBMs in Relation to Security of DataA Comparative Study of RDBMs and OODBMs in Relation to Security of Data
A Comparative Study of RDBMs and OODBMs in Relation to Security of Datainscit2006
 
iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...
iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...
iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...inscit2006
 
Designing People’s Interconnections in Mobile Social Networks
Designing People’s Interconnections in Mobile Social NetworksDesigning People’s Interconnections in Mobile Social Networks
Designing People’s Interconnections in Mobile Social Networksinscit2006
 
Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...
Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...
Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...inscit2006
 
Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...
Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...
Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...inscit2006
 
Visualization of Multidimensional Information from Scientific Computations
Visualization of Multidimensional Information from Scientific ComputationsVisualization of Multidimensional Information from Scientific Computations
Visualization of Multidimensional Information from Scientific Computationsinscit2006
 
High dimensional Data Visualization using Star Coordinates on Three Dimensions
High dimensional Data Visualization using Star Coordinates on Three DimensionsHigh dimensional Data Visualization using Star Coordinates on Three Dimensions
High dimensional Data Visualization using Star Coordinates on Three Dimensionsinscit2006
 
Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...
Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...
Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...inscit2006
 
Identificación de Nombres de Genes en la Literatura Biomédica
Identificación de Nombres de Genes en la Literatura BiomédicaIdentificación de Nombres de Genes en la Literatura Biomédica
Identificación de Nombres de Genes en la Literatura Biomédicainscit2006
 
Requirement analysis for mobile information exchange in the police using a ti...
Requirement analysis for mobile information exchange in the police using a ti...Requirement analysis for mobile information exchange in the police using a ti...
Requirement analysis for mobile information exchange in the police using a ti...inscit2006
 
Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...
Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...
Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...inscit2006
 
Information updated and conveyed by the neural network systems
Information updated and conveyed by the neural network systemsInformation updated and conveyed by the neural network systems
Information updated and conveyed by the neural network systemsinscit2006
 
Mensajería instantánea: una puerta para una nueva percepción del mundo para n...
Mensajería instantánea: una puerta para una nueva percepción del mundo para n...Mensajería instantánea: una puerta para una nueva percepción del mundo para n...
Mensajería instantánea: una puerta para una nueva percepción del mundo para n...inscit2006
 
How Task Analysis Can Prime the Design of Context-Aware Technologies
How Task Analysis Can Prime the Design of Context-Aware TechnologiesHow Task Analysis Can Prime the Design of Context-Aware Technologies
How Task Analysis Can Prime the Design of Context-Aware Technologiesinscit2006
 

More from inscit2006 (20)

Information Searcher-Provider Fit through Information Presentation and Visual...
Information Searcher-Provider Fit through Information Presentation and Visual...Information Searcher-Provider Fit through Information Presentation and Visual...
Information Searcher-Provider Fit through Information Presentation and Visual...
 
Difference of application of fuzzy rough sets and probability random on targe...
Difference of application of fuzzy rough sets and probability random on targe...Difference of application of fuzzy rough sets and probability random on targe...
Difference of application of fuzzy rough sets and probability random on targe...
 
The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...
The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...
The Interaction of Navigation Instructions and Visual Attention in Dynamic Au...
 
Weighted Naïve Bayes Model for Semi-Structured Document Categorization
Weighted Naïve Bayes Model for Semi-Structured Document CategorizationWeighted Naïve Bayes Model for Semi-Structured Document Categorization
Weighted Naïve Bayes Model for Semi-Structured Document Categorization
 
The role of education within the framework of information sciences and techno...
The role of education within the framework of information sciences and techno...The role of education within the framework of information sciences and techno...
The role of education within the framework of information sciences and techno...
 
A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...
A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...
A Metadata-Driven Approach to Computing Financial Analytics in a Relational D...
 
A Comparative Study of RDBMs and OODBMs in Relation to Security of Data
A Comparative Study of RDBMs and OODBMs in Relation to Security of DataA Comparative Study of RDBMs and OODBMs in Relation to Security of Data
A Comparative Study of RDBMs and OODBMs in Relation to Security of Data
 
iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...
iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...
iCE- Interactive Co-innovation Environment Software, Spatial Mapping Tools fo...
 
Designing People’s Interconnections in Mobile Social Networks
Designing People’s Interconnections in Mobile Social NetworksDesigning People’s Interconnections in Mobile Social Networks
Designing People’s Interconnections in Mobile Social Networks
 
Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...
Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...
Visual Literacy: A Semiotic Analysis of Icons as Visual Information Represent...
 
Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...
Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...
Visualizing Search Term Relevance, Boolean Operators, and Phrases using the C...
 
Visualization of Multidimensional Information from Scientific Computations
Visualization of Multidimensional Information from Scientific ComputationsVisualization of Multidimensional Information from Scientific Computations
Visualization of Multidimensional Information from Scientific Computations
 
High dimensional Data Visualization using Star Coordinates on Three Dimensions
High dimensional Data Visualization using Star Coordinates on Three DimensionsHigh dimensional Data Visualization using Star Coordinates on Three Dimensions
High dimensional Data Visualization using Star Coordinates on Three Dimensions
 
Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...
Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...
Knowledge Discovery in Environmental Impact Report’s summary texts: an explor...
 
Identificación de Nombres de Genes en la Literatura Biomédica
Identificación de Nombres de Genes en la Literatura BiomédicaIdentificación de Nombres de Genes en la Literatura Biomédica
Identificación de Nombres de Genes en la Literatura Biomédica
 
Requirement analysis for mobile information exchange in the police using a ti...
Requirement analysis for mobile information exchange in the police using a ti...Requirement analysis for mobile information exchange in the police using a ti...
Requirement analysis for mobile information exchange in the police using a ti...
 
Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...
Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...
Parametric Study to Enhance Genetic Algorithm's Performance using Ranked base...
 
Information updated and conveyed by the neural network systems
Information updated and conveyed by the neural network systemsInformation updated and conveyed by the neural network systems
Information updated and conveyed by the neural network systems
 
Mensajería instantánea: una puerta para una nueva percepción del mundo para n...
Mensajería instantánea: una puerta para una nueva percepción del mundo para n...Mensajería instantánea: una puerta para una nueva percepción del mundo para n...
Mensajería instantánea: una puerta para una nueva percepción del mundo para n...
 
How Task Analysis Can Prime the Design of Context-Aware Technologies
How Task Analysis Can Prime the Design of Context-Aware TechnologiesHow Task Analysis Can Prime the Design of Context-Aware Technologies
How Task Analysis Can Prime the Design of Context-Aware Technologies
 

Recently uploaded

Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 

Recently uploaded (20)

Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 

Natural Language Understanding System Explained

  • 1. An Intuitive Natural Language Understanding System Vanitadevi Patil
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39. The response analysis of four different types of structures 00 12 12 Wh-question 01 07 08 Yes-No Question 02 14 16 Imperative 00 14 14 Declarative Number of wrong responses Number of Correct responses Number of Sentences Sentence Type
  • 40.
  • 41.
  • 42.