SlideShare ist ein Scribd-Unternehmen logo

NLP new words

•Als PPT, PDF herunterladen•
•
G
guest9fc47a

sdf

TechnologieBildung
1 von 86
CS60057 Speech &Natural Language Processing Autumn 2007 Lecture 5 2 August 2007
WORDS The Building Blocks of Language
[object Object],[object Object]
Tokens, Types and Texts ,[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],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extracting text from the Web ,[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],[object Object],[object Object]
Extracting text from NLTK Corpora ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Brown Corpus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Corpus Linguistics ,[object Object],[object Object],[object Object],[object Object],[object Object]
What’s a word? ,[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]
Another example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Some Useful Empirical Observations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Common words in Tom Sawyer but words in NL have an uneven distribution…
Text properties (formalized) Sample word frequency data
Frequency of frequencies ,[object Object],[object Object],[object Object],[object Object],[object Object]
Zipf’s Law ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Zipf’s Law ,[object Object]
Zipf curve
Predicting Occurrence Frequencies ,[object Object],[object Object],[object Object],[object Object],Fraction of words with frequency n is: Fraction of words appearing only once is therefore ½.
Explanations for Zipf’s Law ,[object Object]
Zipf’s First Law ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Zipf’s Second Law ,[object Object],[object Object],[object Object]
Zipf’s Third Law ,[object Object],[object Object],[object Object]
Zipf’s Law Impact on Language Analysis ,[object Object],[object Object]
Vocabulary Growth ,[object Object],[object Object],[object Object]
Heaps’ Law ,[object Object],[object Object],[object Object],[object Object]
Heaps’ Law Data
Word counts are interesting... ,[object Object],[object Object],[object Object],[object Object],[object Object]
Zipf’s Law on Tom Saywer ,[object Object],[object Object],[object Object],[object Object]
Plot of Zipf’s Law ,[object Object],[object Object]
Plot of Zipf’s Law (con’t) ,[object Object],[object Object]
Zipf’s Law, so what? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
N-Grams and Corpus Linguistics
A bad language model N-grams & Language Modeling
A bad language model
A bad language model Herman is reprinted with permission from LaughingStock Licensing Inc., Ottawa Canada. All rights reserved.
What’s a Language Model ,[object Object],[object Object],[object Object]
What’s a language model for? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Next Word Prediction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Human Word Prediction ,[object Object],[object Object],[object Object],[object Object],[object Object]
Claim ,[object Object],[object Object]
Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simple N-Grams ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
N-grams ,[object Object],[object Object],[object Object],[object Object]
Computing the Probability of a Word Sequence ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bigram Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using N-Grams ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The n -gram Approximation ,[object Object],[object Object],[object Object],[object Object],[object Object]
n- grams, continued ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
N-grams for Language Generation ,[object Object],Unigram: 5. …Here words are chosen independently but with their appropriate frequencies. REPRESENTING AND SPEEDILY IS AN GOOD APT OR COME CAN DIFFERENT NATURAL HERE HE THE A IN CAME THE TO OF TO EXPERT GRAY COME TO FURNISHES THE LINE MESSAGE HAD BE THESE. Bigram: 6. Second-order word approximation. The word transition probabilities are correct but no further structure is included. THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHARACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THAT THE TIME OF WHO EVER TOLD THE PROBLEM FOR AN UNEXPECTED.
N-Gram Models of Language ,[object Object],[object Object],[object Object],[object Object],[object Object]
Counting Words in Corpora ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Terminology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Corpora ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simple N-Grams ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Computing the Probability of a Word Sequence ,[object Object],[object Object],[object Object],[object Object],[object Object]
Bigram Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using N-Grams ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Training and Testing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A Simple Example ,[object Object],[object Object]
A Bigram Grammar Fragment from BERP .001 Eat British .03 Eat today .007 Eat dessert .04 Eat Indian .01 Eat tomorrow .04 Eat a .02 Eat Mexican .04 Eat at .02 Eat Chinese .05 Eat dinner .02 Eat in .06 Eat lunch .03 Eat breakfast .06 Eat some .03 Eat Thai .16 Eat on
.01 British lunch .05 Want a .01 British cuisine .65 Want to .15 British restaurant .04 I have .60 British food .08 I don’t .02 To be .29 I would .09 To spend .32 I want .14 To have .02 <start> I’m .26 To eat .04 <start> Tell .01 Want Thai .06 <start> I’d .04 Want some .25 <start> I
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
BERP Bigram Counts 0 1 0 0 0 0 4 Lunch 0 0 0 0 17 0 19 Food 1 120 0 0 0 0 2 Chinese 52 2 19 0 2 0 0 Eat 12 0 3 860 10 0 3 To 6 8 6 0 786 0 3 Want 0 0 0 13 0 1087 8 I lunch Food Chinese Eat To Want I
BERP Bigram Probabilities ,[object Object],[object Object],[object Object],[object Object],[object Object],459 1506 213 938 3256 1215 3437 Lunch Food Chinese Eat To Want I
What do we learn about the language? ,[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]
Approximating Shakespeare ,[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],[object Object],[object Object]
N-Gram Training Sensitivity ,[object Object],[object Object]
Some Useful Empirical Observations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Smoothing Techniques ,[object Object],[object Object],[object Object]
Smoothing Techniques ,[object Object],[object Object],[object Object]
Add-one Smoothing ,[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],[object Object],[object Object],Witten-Bell Discounting
[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Good-Turing Discounting
Backoff methods (e.g. Katz ‘87) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object]

Empfohlen

MODULE 4-Text Analytics.pptx
MODULE 4-Text Analytics.pptxMODULE 4-Text Analytics.pptx
MODULE 4-Text Analytics.pptx
nikshaikh786
 
Treebank annotation
Treebank annotationTreebank annotation
Treebank annotation
Mohit Jasapara
 
PUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINEPUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINE
Abhishek Shivhare
 
Text similarity measures
Text similarity measuresText similarity measures
Text similarity measures
ankit_ppt
 
Tutorial on word2vec
Tutorial on word2vecTutorial on word2vec
Tutorial on word2vec
Leiden University
 
NLP_KASHK:Evaluating Language Model
NLP_KASHK:Evaluating Language ModelNLP_KASHK:Evaluating Language Model
NLP_KASHK:Evaluating Language Model
Hemantha Kulathilake
 
Texture in image processing
Texture in image processing Texture in image processing
Texture in image processing
Anna Aquarian
 
Lecture on classical and quantum information
Lecture on classical and quantum informationLecture on classical and quantum information
Lecture on classical and quantum information
Krzysztof Pomorski
 

Empfohlen

MODULE 4-Text Analytics.pptx
MODULE 4-Text Analytics.pptxMODULE 4-Text Analytics.pptx
MODULE 4-Text Analytics.pptx
nikshaikh786
 
Treebank annotation
Treebank annotationTreebank annotation
Treebank annotation
Mohit Jasapara
 
PUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINEPUSH DOWN AUTOMATA VS TURING MACHINE
PUSH DOWN AUTOMATA VS TURING MACHINE
Abhishek Shivhare
 
Text similarity measures
Text similarity measuresText similarity measures
Text similarity measures
ankit_ppt
 
Tutorial on word2vec
Tutorial on word2vecTutorial on word2vec
Tutorial on word2vec
Leiden University
 
NLP_KASHK:Evaluating Language Model
NLP_KASHK:Evaluating Language ModelNLP_KASHK:Evaluating Language Model
NLP_KASHK:Evaluating Language Model
Hemantha Kulathilake
 
Texture in image processing
Texture in image processing Texture in image processing
Texture in image processing
Anna Aquarian
 
Lecture on classical and quantum information
Lecture on classical and quantum informationLecture on classical and quantum information
Lecture on classical and quantum information
Krzysztof Pomorski
 
Nlp
NlpNlp
Nlp
Nishanthini Mary
 
Document similarity
Document similarityDocument similarity
Document similarity
Hemant Hatankar
 
Multimedia lossy compression algorithms
Multimedia lossy compression algorithmsMultimedia lossy compression algorithms
Multimedia lossy compression algorithms
Mazin Alwaaly
 
ASCII and Unicode.pptx
ASCII and Unicode.pptxASCII and Unicode.pptx
ASCII and Unicode.pptx
KhalilJary
 
Natural language processing
Natural language processingNatural language processing
Natural language processing
National Institute of Technology Durgapur
 
Text summarization
Text summarizationText summarization
Text summarization
Akash Karwande
 
NLP Applications
NLP ApplicationsNLP Applications
NLP Applications
Repustate
 
Lz77 (sliding window)
Lz77 (sliding window)Lz77 (sliding window)
Lz77 (sliding window)
MANISH T I
 
Structure from motion
Structure from motionStructure from motion
Structure from motion
Fatima Radi
 
A Panorama of Natural Language Processing
A Panorama of Natural Language ProcessingA Panorama of Natural Language Processing
A Panorama of Natural Language Processing
Ted Xiao
 
Natural language processing (nlp)
Natural language processing (nlp)Natural language processing (nlp)
Natural language processing (nlp)
Kuppusamy P
 
Introduction to text to speech
Introduction to text to speechIntroduction to text to speech
Introduction to text to speech
Bilgin Aksoy
 
Machine translation
Machine translationMachine translation
Machine translation
mohamed hassan
 
Word embedding
Word embedding Word embedding
Word embedding
ShivaniChoudhary74
 
Introduction to Natural Language Processing (NLP)
Introduction to Natural Language Processing (NLP)Introduction to Natural Language Processing (NLP)
Introduction to Natural Language Processing (NLP)
VenkateshMurugadas
 
Word embeddings
Word embeddingsWord embeddings
Word embeddings
Shruti kar
 
Mathematics:Cryptography
Mathematics:CryptographyMathematics:Cryptography
Mathematics:Cryptography
St Mary's College,Thrissur,Kerala
 
Introduction to Operational Semantics
Introduction to Operational Semantics Introduction to Operational Semantics
Introduction to Operational Semantics
jsinglet
 
Lzw
LzwLzw
Lzw
Daniel A
 
Artificial Neural Networks Lect1: Introduction & neural computation
Artificial Neural Networks Lect1: Introduction & neural computationArtificial Neural Networks Lect1: Introduction & neural computation
Artificial Neural Networks Lect1: Introduction & neural computation
Mohammed Bennamoun
 
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
captainmactavish1996
 
Lecture 7- Text Statistics and Document Parsing
Lecture 7- Text Statistics and Document ParsingLecture 7- Text Statistics and Document Parsing
Lecture 7- Text Statistics and Document Parsing
Sean Golliher
 

Weitere ähnliche Inhalte

Was ist angesagt?

Text summarization
Text summarizationText summarization
Text summarization
Akash Karwande
 
Natural language processing (nlp)
Natural language processing (nlp)Natural language processing (nlp)
Natural language processing (nlp)
Kuppusamy P
 

Was ist angesagt? (20)

Nlp
NlpNlp
Nlp
 
Document similarity
Document similarityDocument similarity
Document similarity
 
Multimedia lossy compression algorithms
Multimedia lossy compression algorithmsMultimedia lossy compression algorithms
Multimedia lossy compression algorithms
 
ASCII and Unicode.pptx
ASCII and Unicode.pptxASCII and Unicode.pptx
ASCII and Unicode.pptx
 
Natural language processing
Natural language processingNatural language processing
Natural language processing
 
Text summarization
Text summarizationText summarization
Text summarization
 
NLP Applications
NLP ApplicationsNLP Applications
NLP Applications
 
Lz77 (sliding window)
Lz77 (sliding window)Lz77 (sliding window)
Lz77 (sliding window)
 
Structure from motion
Structure from motionStructure from motion
Structure from motion
 
A Panorama of Natural Language Processing
A Panorama of Natural Language ProcessingA Panorama of Natural Language Processing
A Panorama of Natural Language Processing
 
Natural language processing (nlp)
Natural language processing (nlp)Natural language processing (nlp)
Natural language processing (nlp)
 
Introduction to text to speech
Introduction to text to speechIntroduction to text to speech
Introduction to text to speech
 
Machine translation
Machine translationMachine translation
Machine translation
 
Word embedding
Word embedding Word embedding
Word embedding
 
Introduction to Natural Language Processing (NLP)
Introduction to Natural Language Processing (NLP)Introduction to Natural Language Processing (NLP)
Introduction to Natural Language Processing (NLP)
 
Word embeddings
Word embeddingsWord embeddings
Word embeddings
 
Mathematics:Cryptography
Mathematics:CryptographyMathematics:Cryptography
Mathematics:Cryptography
 
Introduction to Operational Semantics
Introduction to Operational Semantics Introduction to Operational Semantics
Introduction to Operational Semantics
 
Lzw
LzwLzw
Lzw
 
Artificial Neural Networks Lect1: Introduction & neural computation
Artificial Neural Networks Lect1: Introduction & neural computationArtificial Neural Networks Lect1: Introduction & neural computation
Artificial Neural Networks Lect1: Introduction & neural computation
 

Ähnlich wie NLP new words

Coms30123 Synthesis 3 Projector
Coms30123 Synthesis 3 ProjectorComs30123 Synthesis 3 Projector
Coms30123 Synthesis 3 Projector
Dr. Cupid Lucid
 
Chapter 2 Text Operation and Term Weighting.pdf
Chapter 2 Text Operation and Term Weighting.pdfChapter 2 Text Operation and Term Weighting.pdf
Chapter 2 Text Operation and Term Weighting.pdf
JemalNesre1
 
Stemming algorithms
Stemming algorithmsStemming algorithms
Stemming algorithms
Raghu nath
 
Natural Language Processing made easy
Natural Language Processing made easyNatural Language Processing made easy
Natural Language Processing made easy
Gopi Krishnan Nambiar
 
Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing
Mustafa Jarrar
 

Ähnlich wie NLP new words (20)

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
 
Lecture 7- Text Statistics and Document Parsing
Lecture 7- Text Statistics and Document ParsingLecture 7- Text Statistics and Document Parsing
Lecture 7- Text Statistics and Document Parsing
 
crypto_graphy_PPTs.pdf
crypto_graphy_PPTs.pdfcrypto_graphy_PPTs.pdf
crypto_graphy_PPTs.pdf
 
Chapter 2 Text Operation.pdf
Chapter 2 Text Operation.pdfChapter 2 Text Operation.pdf
Chapter 2 Text Operation.pdf
 
Computational linguistics
Computational linguisticsComputational linguistics
Computational linguistics
 
Coms30123 Synthesis 3 Projector
Coms30123 Synthesis 3 ProjectorComs30123 Synthesis 3 Projector
Coms30123 Synthesis 3 Projector
 
Introduction to linguistics
Introduction to linguisticsIntroduction to linguistics
Introduction to linguistics
 
Chapter 2 Text Operation and Term Weighting.pdf
Chapter 2 Text Operation and Term Weighting.pdfChapter 2 Text Operation and Term Weighting.pdf
Chapter 2 Text Operation and Term Weighting.pdf
 
OpenNLP demo
OpenNLP demoOpenNLP demo
OpenNLP demo
 
Ir 03
Ir 03Ir 03
Ir 03
 
Stemming algorithms
Stemming algorithmsStemming algorithms
Stemming algorithms
 
NLTK: Natural Language Processing made easy
NLTK: Natural Language Processing made easyNLTK: Natural Language Processing made easy
NLTK: Natural Language Processing made easy
 
Natural Language Processing made easy
Natural Language Processing made easyNatural Language Processing made easy
Natural Language Processing made easy
 
Linguistics
LinguisticsLinguistics
Linguistics
 
Natural Language parsing.pptx
Natural Language parsing.pptxNatural Language parsing.pptx
Natural Language parsing.pptx
 
ToC_M1L3_Grammar and Derivation.pdf
ToC_M1L3_Grammar and Derivation.pdfToC_M1L3_Grammar and Derivation.pdf
ToC_M1L3_Grammar and Derivation.pdf
 
Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing
 
NLP_guest_lecture.pdf
NLP_guest_lecture.pdfNLP_guest_lecture.pdf
NLP_guest_lecture.pdf
 
Natural Language processing Parts of speech tagging, its classes, and how to ...
Natural Language processing Parts of speech tagging, its classes, and how to ...Natural Language processing Parts of speech tagging, its classes, and how to ...
Natural Language processing Parts of speech tagging, its classes, and how to ...
 
Nlp
NlpNlp
Nlp
 

Kürzlich hochgeladen

Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
WSO2
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
UiPathCommunity
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Angeliki Cooney
 

Kürzlich hochgeladen (20)

Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 

NLP new words

  • 1. CS60057 Speech &Natural Language Processing Autumn 2007 Lecture 5 2 August 2007
  • 2. WORDS The Building Blocks of Language
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.  
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Common words in Tom Sawyer but words in NL have an uneven distribution…
  • 16. Text properties (formalized) Sample word frequency data
  • 17.
  • 18.
  • 19.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35. N-Grams and Corpus Linguistics
  • 36. A bad language model N-grams & Language Modeling
  • 37. A bad language model
  • 38. A bad language model Herman is reprinted with permission from LaughingStock Licensing Inc., Ottawa Canada. All rights reserved.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66. A Bigram Grammar Fragment from BERP .001 Eat British .03 Eat today .007 Eat dessert .04 Eat Indian .01 Eat tomorrow .04 Eat a .02 Eat Mexican .04 Eat at .02 Eat Chinese .05 Eat dinner .02 Eat in .06 Eat lunch .03 Eat breakfast .06 Eat some .03 Eat Thai .16 Eat on
  • 67. .01 British lunch .05 Want a .01 British cuisine .65 Want to .15 British restaurant .04 I have .60 British food .08 I don’t .02 To be .29 I would .09 To spend .32 I want .14 To have .02 <start> I’m .26 To eat .04 <start> Tell .01 Want Thai .06 <start> I’d .04 Want some .25 <start> I
  • 68.
  • 69. BERP Bigram Counts 0 1 0 0 0 0 4 Lunch 0 0 0 0 17 0 19 Food 1 120 0 0 0 0 2 Chinese 52 2 19 0 2 0 0 Eat 12 0 3 860 10 0 3 To 6 8 6 0 786 0 3 Want 0 0 0 13 0 1087 8 I lunch Food Chinese Eat To Want I
  • 70.
  • 71.
  • 72.
  • 73.
  • 74.
  • 75.
  • 76.
  • 77.
  • 78.
  • 79.
  • 80.
  • 81.
  • 82.
  • 83.
  • 84.
  • 85.
  • 86.