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natural_language_processing_with_spark_nlp_index

Table of Contents

Natural Language Processing with Spark NLP Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Z
Fair Use Sources

Natural Language Processing with Spark NLP Index

Return to NLP Glossary, Natural Language Processing with Spark NLP, NLP bibliography - Python NLP - NLP, Python AI - AI bibliography, Python ML - Machine Learning (ML) bibliography, Python DL - Deep Learning (DL) bibliography, Python Data science - Data Science bibliography

A

abjads, Abjads
Addin, Saif, Background
alphabets, Alphabets
Anaconda, Other Tools
analytic languages, Morphology, Morphological Complexity
annotation libraries, Annotation Libraries
annotation, in document-annotation model, Annotation Libraries
annotators
defined, Hello World with Spark NLP
in document-annotation model, Annotation Libraries
Lemmatizer, Lemmatizer
POS tagger, POS tagger
SentenceDetector, SentenceDetector
Spark NLP library, Annotators-POS tagger
Tokenizer, Tokenizer
ANNs (artificial neural networks), Deep Learning Basics
Apache Hadoop, MapReduce and Apache Hadoop
Apache Hive, MapReduce and Apache Hadoop
Apache Pig, MapReduce and Apache Hadoop
Apache Spark (basics), Apache Spark
architecture, Architecture of Apache Spark-Python and R
getting familiar with, Getting Familiar with Apache Spark-Loading and Viewing Data in Apache Spark
loading/viewing data with, Loading and Viewing Data in Apache Spark-Loading and Viewing Data in Apache Spark
logical architecture, Logical Architecture-Python and R
natural language processing, NLP on Apache Spark-Resources
physical architecture, Physical Architecture
resources, Resources
setting up/starting, Prerequisites
SparkSQL and Spark MLlib, Spark SQL and Spark MLlib-Serialization of models
(see also MLlib)
starting with Spark NLP, Starting Apache Spark with Spark NLP
App, Spark jobs and, Spark jobs
Arabic writing system, Abjads
architecture, Apache Spark, Architecture of Apache Spark-Python and R
logical architecture, Logical Architecture-Python and R
physical architecture, Physical Architecture
Argument Reasoning Comprehension Task, ELMo, BERT, and XLNet
artificial neural networks (ANNs), Deep Learning Basics
ASCII (American Standard Code for Information Interchange), ASCII
ASCII tokenizer, Exercises: Tokenizing
assertion status detection, Assertion Status Detection-Assertion Status Detection
attention modeling, Transformers

B

backpropagation, Backpropagation-Backpropagation
backpropagation through time (BPTT), Backpropagation Through Time
bag (multiset), Bag-of-Words
bag of words, Bag-of-Words-CountVectorizer
in classification/regression, Bag-of-Words Features
CountVectorizer, Bag-of-Words-CountVectorizer
BasicPipeLine, Explain document ML pipeline
Baum-Welch algorithm, (Hidden) Markov Models
Bidirectional Encoder Representations from Transformers (BERT), ELMo, BERT, and XLNet
Binarizer, Binarizer
boosts, Implement the Solution
bound content morphemes, Morphology
bound functional morphemes, Morphology
BPTT (backpropagation through time), Backpropagation Through Time
Branzan, Claudiu, Background

C

cache, Spark NLP Model Cache
categories, in wikis, Implement the Solution
CBOW (continuous bag-of-word)
distributed memory paragraph vectors, doc2vec
Word2Vec implementation, Word2vec
character N-grams, Exercise: Character N-Grams
chatbot, Chatbot-Conclusion
business metrics, Business Metrics
designing the solution, Design the Solution
implementing the solution, Implement the Solution-Implement the Solution
model-centric metrics, Model-Centric Metrics
planning the project, Plan the Project
problem statement and constrain ts, Problem Statement and Constrain ts
review process, Review
testing/measuring the solution, Test and Measure the Solution
chunking, Chunking and Syntactic Parsing
classification and regression, Classification and Regression-Exercises
academic paper classification, Scenario: Academic Paper Classification, Scenario: Academic Paper Classification
bag-of-words features, Bag-of-Words Features
exercises, Exercises
feature selection, Feature Selection-Feature Selection
iteration cycle, Iteration-Iteration
modeling, Modeling
regular expression features, Regular Expression Features
client mode, Physical Architecture
cluster mode, Physical Architecture
clustering, Parallelism, Concurrency, Distributing Computation
CNNs (see convolutional neural networks)
compound words, Compound Words
concurrency, Parallelism, Concurrency, Distributing Computation
conditional random fields (CRFs), Conditional Random Field
configuration data, Managing Data Resources
content morphemes, Morphology
context
semantics and, Topic Modeling
sociolinguistics and, Context
continuous skip grams, Word2vec
convolutional neural networks (CNN), Convolutional Neural Networks
filters, Filters
pooling, Pooling
coreference resolution, Coreference Resolution
cosine similarity, Latent Semantic Indexing
CountVectorizer, Bag-of-Words-CountVectorizer
CRFs (conditional random fields), Conditional Random Field
cross entropy (log-loss), Model-Centric Metrics
CrossValidator, Cross validation
cumulative gain (index metric), Model-Centric Metrics
Cyrillic alphabet, Alphabets

D

DAG (directed acyclic graph), Spark jobs, Spark Optimization Basics
data
loading/viewing with Apache Spark, Loading and Viewing Data in Apache Spark-Loading and Viewing Data in Apache Spark
managing data resources, Managing Data Resources
in NLP context, Why Natural Language Processing Is Important and Difficult
DataFrame
showing output of, Loading and Viewing Data in Apache Spark-Loading and Viewing Data in Apache Spark
Spark optimization and, Spark Optimization Basics
Dataset, Spark SQL and Spark MLlib
decision trees, Decision/Regression Trees
DecisionTreeClassifier, StringIndexer
deep learning, Deep Learning Basics-Resources
backpropagation, Backpropagation-Backpropagation
basics, Deep Learning Basics-Resources
convolutional neural networks, Convolutional Neural Networks
exercises, Exercise 1
gradient descent, Gradient Descent
multilingual, Transfer Learning and Multilingual Deep Learning
perceptron algorithm, Deep Learning Basics-Deep Learning Basics
recurrent neural networks, Recurrent Neural Networks-LSTMs
resources, Resources
deep learning algorithms, Deep Learning Algorithms
definiteness, Named-Entity Recognition
deixis, Named-Entity Recognition
demoing NLP-based applications, Demoing NLP-Based Applications
deployment in production environment (see productionizing NLP applications)
derivational affixes, Morphology
Devanagari writing system, Abugidas
diacritics, Alphabets
dialects, Sociolinguistics: Dialects, Registers, and Other Varieties
Dias, Kevin, (Hidden) Markov Models
directed acyclic graph (DAG), Spark jobs, Spark Optimization Basics
discourse, Chatbot
distributed bag-of-words paragraph vectors, doc2vec
distributed computing, Parallelism, Concurrency, Distributing Computation
distributed memory paragraph vectors, doc2vec
distributional semantics, Topic Modeling, Word Embeddings
doc2vec, doc2vec
doccano, Implement the Solution-Implement the Solution
Docker, Other Tools
document frequency, Inverse Document Frequency
document search, Information Retrieval
(see also information retrieval)
document, in document-annotation model, Annotation Libraries
document-annotation model, Annotation Libraries
DocumentAssembler, DocumentAssembler
domain-specific NER, Named-Entity Recognition, Named-Entity Recognition

E

edge, defined, Design the Solution
8-bit Unicode Transformation Format (UTF)-8, UTF-8
Elasticsearch, Implement the Solution
elementwise multiplication, Backpropagation
Elman networks, Elman Nets
Elman, Jeffrey, Elman Nets
embeddings from language models (ELMo), ELMo, BERT, and XLNet
(see also word embeddings)
emotion detection (see sentiment analysis and emotion detection)
encoding systems
ASCII, ASCII
Unicode, Unicode
UTF-8, UTF-8
encodings, Encodings-UTF-8
English alphabet, Alphabets
English language
paralinguistic features, Spoken Language Versus Written Language
tokenizing, Tokenize English
environment, setting up, Setting Up Your Environment
Estimators, Estimators and Models-StringIndexer
CrossValidator, Cross validation
MinMaxScaler, MinMaxScaler
Pipelines, Pipelines
StringIndexer, StringIndexer-StringIndexer
Evaluators, Evaluators-Serialization of models
executors, Logical Architecture
expert system, Building Knowledge Bases
Explain Document ML pipeline, Explain document ML pipeline
exploding (vanishing) gradients, Elman Nets

F

G

gain (index metric), Model-Centric Metrics
Ganglia, Monitoring
gaps parameter, Exercises: Tokenizing
Garbe, Wolf, Spelling Correction
Geez (Amharic) writing system, Abugidas, Tokenize Ge’ez (Amharic)
generative models, Language Models
generic NER, Named-Entity Recognition
Gensim topic modeling library, NLP in Other Libraries
GloVe (Global Vectors), GloVe
golden sets, Inter-Labeler Agreement
GPUs (graphics processing units), Spark NLP and TensorFlow Integration
gradient descent, Gradient Descent
graphical models, Sequence Modeling with Keras
Greek alphabet, Alphabets, Tokenize Greek

H

Hadamard product, Backpropagation
Hadoop, MapReduce and Apache Hadoop
Hadoop Distributed File System (HDFS), MapReduce and Apache Hadoop
Han Chinese writing system, Logographs
handwritten text conversion, Images of Handwritten Text to Text
Hangul alphabet, Alphabets
hashing trick, In Spark
HashingTF, In Spark
head-word (lemma), Lemmatization
hedged speech, Assertion Status Detection
hidden Markov models (HMMs), (Hidden) Markov Models-(Hidden) Markov Models
Hiragana writing system, Syllabaries
Hochreiter, Sepp, LSTMs
home signs, Origins of Language
homonymy, Transformers
HTCondor, Parallelization Before Apache Hadoop
Hubel, David H., Convolutional Neural Networks
human labeling, Human Labeling-Conclusion
academic paper classification, Scenario: Academic Paper Classification
checklist for, Checklist
classification, Classification
guidelines, Guidelines-Scenario: Academic Paper Classification
inter-labeler agreement, Inter-Labeler Agreement
iterative labeling, Iterative Labeling
labeling text, Labeling Text
tagging, Tagging
where to find labelers, Scenario: Academic Paper Classification
human language, origins of, Origins of Language

I

IDF (inverse document frequency), Inverse Document Frequency-In Spark
idiolect, Sociolinguistics: Dialects, Registers, and Other Varieties
implied negation, Assertion Status Detection
indefiniteness, Named-Entity Recognition
indexing
inverted indexes, Inverted Indices-Step 4
latent semantic indexing, Latent Semantic Indexing-Latent Semantic Indexing
IndexToString Transformer, StringIndexer
inference engines, Building Knowledge Bases
inflectional affixes, Morphology
information extraction, Information Extraction-Exercises
assertion status detection, Assertion Status Detection-Assertion Status Detection
coreference resolution, Coreference Resolution
exercises, Exercises
named-entity recognition, Named-Entity Recognition-Named-Entity Recognition
relationship extraction, Relationship Extraction-Relationship Extraction
information retrieval, Information Retrieval-Resources
(see also search engines)
exercises, Exercises
inverted indexes, Inverted Indices-Step 4
resources, Resources
stop word removal, Stop-Word Removal
vector space model, Vector Space Model-In Spark
input data, Managing Data Resources
integration tests, Integration Tests
intents, Chatbot
inter-labeler agreement, Inter-Labeler Agreement
International Phonetic Alphabet (IPA), Phonetics and Phonology
inverse document frequency (IDF), Inverse Document Frequency-In Spark
inverted indexes, Inverted Indices-Step 4
Iris Data Set, Spark SQL and Spark MLlib
isolating languages, Morphology
iteration cycle, classification/regression, Iteration-Iteration
iterative labeling, Iterative Labeling

J

Jakobson model of pragmatics, Roman Jakobson
Jakobson, Roman, Roman Jakobson
Java Micro benchmark Harness (JMH), Profiling Tools
Java Virtual Machine (JVM), Parallelism, Concurrency, Distributing Computation
Jupyter Notebook, Other Tools

K

K-Means clustering, K-Means-K-Means
Keras (see sequence modeling with Keras)
kernel, Starting Apache Spark with Spark NLP
knowledge bases
building, Building Knowledge Bases-Conclusion
business metrics, Business Metrics
defined, Building Knowledge Bases
designing the solution, Design the Solution
implementing the solution, Implement the Solution-Implement the Solution
infrastructure metrics, Infrastructure Metrics
model-centric metrics, Model-Centric Metrics
planning the project, Plan the Project
problem statement and constrain ts, Problem Statement and Constrain ts
process metrics, Process Metrics
review process, Review
testing/measuring the solution, Test and Measure the Solution-Process Metrics

L

labeler fatigue, Classification
labeling, human (see human labeling)
language models, Language Models
language topology, Language Typology
language, human
origins of, Origins of Language
spoken versus written, Spoken Language Versus Written Language
languages, supporting multiple, Supporting Multiple Languages-Conclusion
checklist for, Checklist
compound words, Compound Words
language topology, Language Typology
morphological complexity, Morphological Complexity
searching across languages, Search Across Languages
text processing in different languages, Text Processing in Different Languages-Morphological Complexity
transfer learning and multilingual deep learning, Transfer Learning and Multilingual Deep Learning
latent Dirichlet allocation (LDA), Latent Dirichlet Allocation-Latent Dirichlet Allocation
latent semantic indexing (LSI), Latent Semantic Indexing-Latent Semantic Indexing
LeCun, Yann, Convolutional Neural Networks
lemma (head-word), Lemmatization
lemmatization, Lemmatization-Stemming Versus Lemmatization
Lemmatizer, Lemmatizer
letterbox, Origins
lexical morphemes, Morphology
libraries (see specific libraries)
linear models, Linear Models
linear separability, Deep Learning Basics
linguistics, Linguistics-Semantics
morphology, Morphology
phonetics and phonology, Phonetics and Phonology
syntax, Syntax
local mode, Physical Architecture
locking, Parallelism, Concurrency, Distributing Computation
log-loss, Model-Centric Metrics
logging, Output and logging, Design-Level Optimization
logical architecture, Apache Spark
jobs, Spark jobs
ordering, Ordering
output and logging, Output and logging
partitioning, Partitioning
persisting data, Persisting
Python and R, Python and R
RDDs, RDDs
serialization, Serialization
logo graphic writing systems, Logographs, Note on Different Writing Systems
Loper, Edward, Functionality Libraries
loss function, Gradient Descent, Backpropagation
Lovins, Julie Beth, Stemming
LSI (latent semantic indexing), Latent Semantic Indexing-Latent Semantic Indexing
LSTM (long [[short-term memory), LSTMs, Recurrent Neural Networks-Recurrent Neural Networks
Lucene search engine library, NLP in Other Libraries
Luhn, Hans Peter, Stop-Word Removal

M

Manning, Christopher, GloVe
map operation, MapReduce and Apache Hadoop
MapReduce, MapReduce and Apache Hadoop
Markov models
hidden Markov models, (Hidden) Markov Models-(Hidden) Markov Models
predicting sequence of words with, Language Models
Markov property, (Hidden) Markov Models
McCulloch, Warren S., Deep Learning Basics
memory space, Parallelism, Concurrency, Distributing Computation
Mikolov, Tomas, Word Embeddings
MinMaxScaler, MinMaxScaler
Minsky, Marvin, Deep Learning Basics
MLlib, Spark SQL and Spark MLlib-Serialization of models
Estimators and Models, Estimators and Models-StringIndexer
Evaluators, Evaluators-Serialization of models
feature storage, Feature Selection
serialization of models, Serialization of models
VectorAssembler, VectorAssembler
model cache, Spark NLP Model Cache
model measurement, offline versus online, Offline Versus Online Model Measurement
modeling, for classification and regression, Modeling
decision/regression trees, Decision/Regression Trees
deep learning algorithms, Deep Learning Algorithms
linear models, Linear Models
naïve Bayes, Naïve Bayes
models
serialization of, Serialization of models
StringIndexer, StringIndexer-StringIndexer
monitoring
NLP applications, Monitoring
offline versus online model measurement, Offline Versus Online Model Measurement
mood, assertion status detection and, Assertion Status Detection
morphemes, Morphology
morphological complexity, Morphological Complexity
morphology, Morphology
motivated sign, Origins of Language
Multiclass Classification Evaluator, Evaluators-Cross validation
multilingual deep learning, Transfer Learning and Multilingual Deep Learning
multiset (bag), Bag-of-Words

N

N-grams, N-Gram
naïve Bayes classifiers, Naïve Bayes
named-entity recognition (NER), Named-Entity Recognition-Named-Entity Recognition
natural language
basics, What Is Natural Language?-Resources
defined, What Is Natural Language?-Spoken Language Versus Written Language
encodings, Encodings-UTF-8
exercise: tokenizing, Exercises: Tokenizing
linguistics, Linguistics-Semantics
origins of human language, Origins of Language
pragmatics, Pragmatics
resources, Resources
sociolinguistics, Sociolinguistics: Dialects, Registers, and Other Varieties-Context
spoken versus written language, Spoken Language Versus Written Language
natural language processing (NLP) basics
Apache Spark and, NLP on Apache Spark-Resources
libraries (see NLP libraries)
parallelism, concurrency, distributing computation, Parallelism, Concurrency, Distributing Computation-MapReduce and Apache Hadoop
resources, Resources
Spark NLP library, Spark NLP-Exercises: Build a Topic Model
Natural Language Tool Kit (NLTK), Functionality Libraries
NDCG (Normalized Discounted Cumulative Gain), Model-Centric Metrics
negation (negative polarity), Assertion Status Detection
NER (named-entity recognition), Named-Entity Recognition-Named-Entity Recognition
neural networks
backpropagation, Backpropagation-Backpropagation
convolutional neural networks, Convolutional Neural Networks
gradient descent, Gradient Descent
recurrent neural networks, Recurrent Neural Networks-LSTMs
NLP libraries, NLP Libraries
annotation libraries, Annotation Libraries
functionality libraries, Functionality Libraries
NLTK, Functionality Libraries
non-NLP libraries with NLP functionality, NLP in Other Libraries
spaCy, Annotation Libraries
Spark NLP library, Spark NLP-Exercises: Build a Topic Model
NLTK (Natural Language Tool Kit), Functionality Libraries
nonfunctional testing, Usability Tests
nonnegative matrix factorization (NMF), Nonnegative Matrix Factorization-Nonnegative Matrix Factorization
normalization, Normalization
Normalized Discounted Cumulative Gain (NDCG), Model-Centric Metrics
Norvig spelling correction algorithm, Spelling Correction

O

object character recognition (OCR), Object Character Recognition-Conclusion
challenges posed by different writing systems, Note on Different Writing Systems
converting images of handwritten text to text, Images of Handwritten Text to Text
converting images of printed text/PDFs to text, Images of Printed Text and PDFs to Text
converting images of text in environment to text, Images of Text in Environment to Text
converting images of text to target, Images of Text to Target
implementing the solution, Implement the Solution-Implement the Solution
Model-Centric metrics, Model-Centric Metrics
OCR tasks, Kinds of OCR Tasks-Note on Different Writing Systems
planning the project, Plan the Project
problem statement and constrain ts, Problem Statement and Constrain ts
review process, Review
testing/measuring the solution, Test and Measure the Solution
one-hot encoding, Backpropagation
ordering of distributed data, Ordering
output data, Managing Data Resources
output, Apache Spark, Output and logging

P

Papert, Seymour, Deep Learning Basics
paragraph2vec (see doc2vec)
paralinguistic features, Spoken Language Versus Written Language
parallelism, Parallelism, Concurrency, Distributing Computation-MapReduce and Apache Hadoop
parallelization, Parallelization Before Apache Hadoop
part-of-speech (POS) tagging, Part-of-Speech Tagging-Conditional Random Field
partitioning, Partitioning
PDF files, converting to text, Images of Printed Text and PDFs to Text
Pennington, Jeffrey, GloVe
perceptron algorithm, Deep Learning Basics-Deep Learning Basics
performance tests, Performance Tests
persist method, Persisting
persistence, Spark Optimization Basics-Spark Optimization Basics
phone, Phonetics and Phonology
phonemes, Phonetics and Phonology
phonetics, Phonetics and Phonology
phonologies, Phonetics and Phonology
phrase structure trees (PSTs), Syntax
Pig Latin, MapReduce and Apache Hadoop
Pipelines
basics, Pipelines
design-level optimization, Design-Level Optimization
pretrained, Pretrained Pipelines
serialization of models, Serialization of models
Pitts, Walter, Deep Learning Basics
polarity, assertion status detection and, Assertion Status Detection
polysemy, Transformers
pooling, Pooling
Porter Stemmer, Stemming
Porter, Martin, Stemming
POS tagger, POS tagger
pragmatics, Pragmatics
Roman Jakobson model, Roman Jakobson
using, How To Use Pragmatics
pretrained Pipelines, Pretrained Pipelines
process, defined, Parallelism, Concurrency, Distributing Computation
productionizing NLP applications, Productionizing NLP Applications-Conclusion
demoing NLP-based applications, Demoing NLP-Based Applications
design-level optimization, Design-Level Optimization
integration tests, Integration Tests
managing data resources, Managing Data Resources
model deployment checklist, Model Deployment Checklist
monitoring, Monitoring
performance tests, Performance Tests
profiling tools, Profiling Tools
sanity tests, Smoke and Sanity Tests
scaling/performance checklist, Scaling and Performance Checklist
smoke tests, Smoke and Sanity Tests
Spark NLP model cache, Spark NLP Model Cache
Spark NLP/TensorFlow integration, Spark NLP and TensorFlow Integration-Demoing NLP-Based Applications
Spark optimization basics, Spark Optimization Basics-Spark Optimization Basics
testing checklist, Testing Checklist
testing NLP-based applications, Testing NLP-Based Applications
unit tests, Unit Tests
usability tests, Usability Tests
PSTs (phrase structure trees), Syntax
publishing (see productionizing NLP applications)
PySpark, Python and R
Python API, Other Tools, Python and R
Python virtual environment, Setting Up Your Environment

Q

R

R API, Python and R
R-expression (referring expression)
coreferences and, Coreference Resolution
defined, Named-Entity Recognition
race condition, Parallelism, Concurrency, Distributing Computation
RDDs (resilient distributed datasets), RDDs
recurrent neural networks (RNNs), Recurrent Neural Networks-LSTMs
backpropagation through time, Backpropagation Through Time
Elman networks, Elman Nets
LSTMs, LSTMs
sequence modeling, Recurrent Neural Networks-Recurrent Neural Networks
redirects, in wikis, Implement the Solution
reduce operation, MapReduce and Apache Hadoop
referents, Named-Entity Recognition
registers, Sociolinguistics: Dialects, Registers, and Other Varieties
context in, Context
formality/informality in, Formality
regression trees, Decision/Regression Trees
regular expression (regex), Regular Expression Features
relationship extraction, Relationship Extraction-Relationship Extraction
resilient distributed datasets (RDDs), RDDs
restricted vocabulary, Bag-of-Words Features
RNNs (see recurrent neural networks)
Rosenblatt, Frank, Deep Learning Basics

S

sanity tests, Smoke and Sanity Tests
SBD (sentence boundary detection) problem, Sentence Segmentation
schema, Spark SQL and Spark MLlib
Schmidhuber, Jürgen, LSTMs
scikit-learn library, NLP in Other Libraries
search engines, Search Engine-Conclusion
building, Search Engine-Conclusion
business metrics, Business Metrics
designing the solution, Design the Solution
implementing the solution, Implement the Solution-Implement the Solution
model-centric metrics, Model-Centric Metrics
planning the project, Plan the Project
problem statement and constrain ts, Problem Statement and Constrain ts
review process, Review
searching across languages, Search Across Languages
testing/measuring the solution, Test and Measure the Solution
section segmentation, Section Segmentation
semantics, Semantics
sentence boundary detection (SBD) problem, Sentence Segmentation
sentence detection, (Hidden) Markov Models
sentence segmentation, Sentence Segmentation-(Hidden) Markov Models
SentenceDetector, SentenceDetector
sentiment analysis and emotion detection, Sentiment Analysis and Emotion Detection-Conclusion
business metrics, Business Metrics
designing the solution, Design the Solution
fallback plans, Fallback Plans
implementing the solution, Implement the Solution-Implement the Solution
infrastructure metrics, Infrastructure Metrics
initial deployment, Initial Deployment
model-centric metrics, Model-Centric Metrics
next steps after deployment, Next Steps
offline versus online model measurement, Offline Versus Online Model Measurement
planning the project, Plan the Project-Plan the Project
problem statement and constrain ts, Problem Statement and Constrain ts
process metrics, Process Metrics
review process, Review-Next Steps
sentiment analysis defined, Sentiment Analysis and Emotion Detection
testing/measuring the solution, Test and Measure the Solution-Offline Versus Online Model Measurement
sequence modeling with Keras, Sequence Modeling with Keras-Resources
chunking and syntactic parsing, Chunking and Syntactic Parsing
exercises, Exercise: Character N-Grams
language models, Language Models
part-of-speech tagging, Part-of-Speech Tagging-Conditional Random Field
resources, Resources
section segmentation, Section Segmentation
sentence segmentation, Sentence Segmentation-(Hidden) Markov Models
sequences, in machine learning, Sequence Modeling with Keras
serialization, Serialization
shingles (see N-grams)
show() method, Loading and Viewing Data in Apache Spark
simple boolean search, Step 4
simple RNNs (SRNNs), Elman Nets
singular value decomposition (SVD), Latent Semantic Indexing
skip grams, Word2vec
smoke tests, Smoke and Sanity Tests
Socher, Richard, GloVe
sociolinguistics, Sociolinguistics: Dialects, Registers, and Other Varieties-Context
context in, Context
formality, Formality
spaCy library, Annotation Libraries
Spark (see Apache Spark)
spark master, Physical Architecture
Spark MLlib, Spark SQL and Spark MLlib-Serialization of models (see MLlib)
Spark NLP library (basics), Spark NLP-Exercises: Build a Topic Model
annotation storage, Annotation Library
annotators, Annotators-POS tagger
building a topic model, Exercises: Build a Topic Model
DocumentAssembler, DocumentAssembler
exercises, Exercises: Build a Topic Model
Finisher, Finisher-Finisher
Hello World with, Hello World with Spark NLP-Hello World with Spark NLP
pretrained Pipelines, Pretrained Pipelines
stages, Stages-POS tagger
starting Apache Spark with, Starting Apache Spark with Spark NLP
Transformers, Transformers
SparkContext, Spark jobs
SparkR, Python and R
SparkSession, Spark jobs
SparkSQL, Spark SQL and Spark MLlib-Serialization of models
sparse vector, Bag-of-Words
speculative mood, Assertion Status Detection
spelling correction, Spelling Correction
spoken language, written language versus, Spoken Language Versus Written Language
SQLTransformer, SQLTransformer
SRNNs (simple RNNs), Elman Nets
stemming, Stemming, Stemming Versus Lemmatization-Stemming Versus Lemmatization
stochastic processes, (Hidden) Markov Models
stop word removal, Stop-Word Removal
StringIndexer, StringIndexer-StringIndexer
SVD (singular value decomposition), Latent Semantic Indexing
syllabaries, Syllabaries
Symmetric Delete, Spelling Correction
syntactic parsing, Chunking and Syntactic Parsing
syntax, Syntax
synthetic (agglutinative) languages, Morphology, Morphological Complexity

T

tagging, Tagging
Talby, David, Background
TensorFlow, Spark NLP and TensorFlow Integration-Demoing NLP-Based Applications
term frequency, Inverse Document Frequency
Tesseract, Implement the Solution-Implement the Solution
testing
checklist for, Testing Checklist
integration tests, Integration Tests
NLP-based applications, Testing NLP-Based Applications
performance tests, Performance Tests
sanity tests, Smoke and Sanity Tests
smoke tests, Smoke and Sanity Tests
unit tests, Unit Tests
usability tests, [`[Usability Test]]s
text (see human labeling; object character recognition)
TF.IDF, Inverse Document Frequency-Inverse Document Frequency
Thai writing system, Abugidas
thread pool, Parallelism, Concurrency, Distributing Computation-Parallelism, Concurrency, Distributing Computation
tokenization, Tokenization-Tokenization
English, Tokenize English
exercise, Exercises: Tokenizing
Greek, Tokenize Greek
Tokenizer, Tokenizer
tone, Spoken Language Versus Written Language
topic modeling, Topic Modeling-Exercises
building a topic model, Exercises: Build a Topic Model
exercises, Exercises-Exercises
K-Means clustering, K-Means-K-Means
latent Dirichlet allocation, Latent Dirichlet Allocation-Latent Dirichlet Allocation
latent semantic indexing, Latent Semantic Indexing-Latent Semantic Indexing
nonnegative matrix factorization, Nonnegative Matrix Factorization-Nonnegative Matrix Factorization
topology, language, Language Typology
transfer learning, fastText, Transfer Learning and Multilingual Deep Learning
transformations, Estimators and Models-StringIndexer
Transformers, Transformers-VectorAssembler
Binarizer, Binarizer
SQLTransformer, SQLTransformer
VectorAssembler, VectorAssembler
word embeddings and, Transformers
tree-based models, Decision/Regression Trees
trie, Named-Entity Recognition-Named-Entity Recognition
Tsalagi writing system, Syllabaries

U

UDFs (user-defined functions), Spark SQL and Spark MLlib
unbound content morphemes, Morphology
unbound functional morphemes, Morphology
underflow, (Hidden) Markov Models
Unicode, Unicode
unit tests, Unit Tests
unmotivated signs, Origins of Language
usability tests, Usability Tests
user-defined functions (UDFs), Spark SQL and Spark MLlib

V

validated labels, Inter-Labeler Agreement
vanishing (exploding) gradients, Elman Nets
vector space model, Vector Space Model-In Spark
VectorAssembler, VectorAssembler
vectors, doc2vec and, doc2vec
verbal dyspraxia, Origins of Language
visualizations, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
word clouds, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
word frequencies, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
VisualVM, Profiling Tools
Viterbi algorithm, (Hidden) Markov Models
vocabulary reduction, Vocabulary Reduction-Stemming Versus Lemmatization
lemmatization, Lemmatization-Stemming Versus Lemmatization
normalization, Normalization
spelling correction, Spelling Correction
stemming, Stemming

W

white space tokenizer, Exercises: Tokenizing, Tokenization-Tokenization
Wiesel, Torsten, Convolutional Neural Networks
wikis (see knowledge bases)
windows, in machine learning, Sequence Modeling with Keras
word clouds, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
word compounding, Compound Words
word embeddings, Word Embeddings-Exercises
BERT, ELMo, BERT, and XLNet
doc2vec, doc2vec
ELMo, ELMo, BERT, and XLNet
fastText, fastText
GloVe, GloVe
transformers, Transformers
Word2Vec, Word2vec-Word2vec
XLNet, ELMo, BERT, and XLNet
word frequency visualizations, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
word language modeling, Exercise: Word Language Model
word processing techniques (for NLP), Processing Words-Resources
bag of words, Bag-of-Words-CountVectorizer
lemmatization, Lemmatization-Stemming Versus Lemmatization
N-grams, N-Gram
normalization, Normalization
resources, Resources
spelling correction, Spelling Correction
stemming, Stemming
tokenization, Tokenization-Tokenization
visualizations, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
vocabulary reduction, Vocabulary Reduction-Stemming Versus Lemmatization
word cloud visualizations, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
word frequency visualizations, Visualizing: Word and Document Distributions-Visualizing: Word and Document Distributions
Word2Vec, Word2vec-Word2vec
writing systems, Writing Systems-Logographs
abjads, Abjads
abugidas, Abugidas
alphabets, Alphabets
logographs, Logographs
origins, Origins
syllabaries, Syllabaries
written language, spoken language versus, Spoken Language Versus Written Language

X

XLNet, ELMo, BERT, and XLNet

Z

Zaharia, Matei, Apache Spark
Zipfs law, Step 4

Fair Use Sources

Fair Use Sources:

B08BW4W4CZ (NLPwSprk 2020)

Natural Language Processing (NLP): What Is Language, Text classification, Language modeling, Google Gemini, ChatGPT

Machine Learning for NLP NLP ML, NLP DL - NLP Deep learning - Python NLP, NLP MLOps, Python NLP (sci-kit NLP, OpenCV NLP, TensorFlow NLP, PyTorch NLP, Keras NLP, NumPy NLP, NLTK NLP, SciPy NLP, sci-kit learn NLP, Seaborn NLP, Matplotlib NLP), C Plus Plus Natural Language Processing | C++ NLP, C Sharp Natural Language Processing | NLP, Golang Natural Language Processing | Golang NLP, Java Natural Language Processing | Java NLP, JavaScript Natural Language Processing | JavaScript NLP, Julia Natural Language Processing | Julia NLP, Kotlin Natural Language Processing | Kotlin NLP, R Natural Language Processing | R NLP, Ruby Natural Language Processing | Ruby NLP, Rust Natural Language Processing | Rust NLP, Scala Natural Language Processing | Scala NLP, Swift Natural Language Processing | Swift NLP, NLP history, NLP bibliography, NLP glossary, NLP topics, NLP courses, NLP libraries, NLP frameworks, NLP GitHub, NLP Awesome list. (navbar_nlp - See also navbar_llm, navbar_chatbot, navbar_dl, navbar_ml, navbar_chatgpt, navbar_ai, borg_usage_disclaimer)

Cloud Monk is Retired ( for now). Buddha with you. © 2025 and Beginningless Time - Present Moment - Three Times: The Buddhas or Fair Use. Disclaimers

SYI LU SENG E MU CHYWE YE. NAN. WEI LA YE. WEI LA YE. SA WA HE.

natural_language_processing_with_spark_nlp_index.txt · Last modified: 2025/02/01 06:40 by 127.0.0.1