Index

A

• antiword tool
  • download link / Microsoft Word documents

B

• Bayes' formula
  • for conditional probability / Bayes' formula for conditional probability
• bias-variance decomposition / Bias-variance decomposition
• bias-variance trade-off / Bias–variance trade-off and learning curve
• binomial distribution / Binomial distribution
• bootstrapping methods / Bootstrap

C

• canonical correspondence analysis (CCA)
  • about / Canonical correspondence analysis
  • Pearson's Chi-squared test / Multiple correspondence analysis
• caret package
  • reference / Stratified
• chunks
  • reference / Chunk tags
• co-occurrences
  • extracting / Extracting co-occurrences
  • surface co-occurrence / Surface Co-occurrence
  • textual co-occurrence / Textual co-occurrence
  • syntactic co-occurrence / Syntactic co-occurrence
  • in document / Co-occurrence in a document
• collocations / N-gram models
• compound probabilities theorem / Theorem of compound probabilities
• concept similarity
  • about / Concept similarity
  • path length / Path length
  • Resnik similarity / Resnik similarity
  • Lin similarity / Lin similarity
  • Jiang-Conrath distance / Jiang – Conrath distance
• conditional probability
  • about / Conditional probability
  • Bayes' formula / Bayes' formula for conditional probability
• confusion matrix
  • about / Confusion matrix
• Correlated topic model (CTM)
  • about / Correlated topic model
  • model selection / Model selection
  • R Package, for topic modeling / R Package for topic modeling
• correspondence analysis
  • about / Correspondence analysis
  • canonical correspondence analysis (CCA) / Canonical correspondence analysis
  • multiple correspondence analysis / Multiple correspondence analysis
• cross-validation
  • about / Cross validation
• cumulative distribution function / Cumulative distribution function

D

• Degree of Reading Power(DRP) / Automated readability index
• dimensionality
  • limitation / The curse of dimensionality
  • distance concentration / Distance concentration and computational infeasibility
  • computational infeasibility / Distance concentration and computational infeasibility
• dimensionality reduction
  • about / Dimensionality reduction
  • principal component analysis (PCA) / Principal component analysis
  • R, using for principal component analysis (PCA) / Using R for PCA
  • reconstruction error / Reconstruction error
• discrete random variables
  • about / Discrete random variables
  • continuous random variables / Continuous random variables
• diverse sources
  • text, accessing / Accessing text from diverse sources
• document clustering
  • about / Document clustering
• document term matrix
  • about / Document term matrix
  • inverse document frequency / Inverse document frequency
  • words similarity / Words similarity and edit-distance functions
  • edit-distance functions / Words similarity and edit-distance functions
  • Euclidean distance / Euclidean distance
  • cosine similarity / Cosine similarity
  • Levenshtein distance / Levenshtein distance
  • Damerau-Levenshtein distance / Damerau-Levenshtein distance
  • Hamming distance / Hamming distance
  • Gunning frog index / Gunning frog index

E

• .exe from
  • download link / Synonymy and similarity
• Easy Listening Formula(ELF) / Automated readability index
• elements
  • entities / Feature extraction
  • attributes / Feature extraction
  • events / Feature extraction
• entity extraction
  • about / Entity extraction
  • rule-based approach / The rule-based approach
  • machine learning / Machine learning
• Extensible Markup Language (XML) / XML

F

• 10-fold cross-validation / k-Fold
• feature extraction
  • about / Feature extraction
  • synonymy / Synonymy and similarity
  • similarity / Synonymy and similarity
  • multiwords / Multiwords, negation, and antonymy
  • negation / Multiwords, negation, and antonymy
  • antonymy / Multiwords, negation, and antonymy
  • concept similarity / Concept similarity
• feature selection, for text clustering
  • about / Feature selection for text clustering
  • mutual information, using / Mutual information
  • statistic Chi Square feature selection / Statistic Chi Square feature selection
  • frequency-based feature selection / Frequency-based feature selection
• file system
  • about / File system
  • PDF documents / PDF documents
  • Microsoft Word documents / Microsoft Word documents
  • Hyper Text Markup Language (HTML) / HTML
  • Extensible Markup Language (XML) / XML
  • JavaScript Object Notation (JSON) / JSON
  • Hypertext Transfer Protocol (HTTP) / HTTP

G

• generative models / Latent Dirichlet Allocation

H

• Hamming distance
  • about / Hamming distance
  • Jaro-Winkler distance / Jaro-Winkler distance
  • text, readability measuring / Measuring readability of a text
• Heaps' laws / Heaps' law
• Hidden Markov Models (HMM), POS tagging
  • about / Hidden Markov Models for POS tagging
  • definitions / Basic definitions and notations
  • notations / Basic definitions and notations
  • implementing / Implementing HMMs
  • Viterbi underflow / Viterbi underflow
  • forward algorithm underflow / Forward algorithm underflow
  • OpenNLP chunking / OpenNLP chunking
  • chunk tags / Chunk tags
• Hyper Text Markup Language (HTML) / HTML
• Hypertext Transfer Protocol (HTTP) / HTTP

I

• independent events
  • for conditional probability / Independent events
• inverse document frequency (IDF) / Inverse document frequency
• Inverse Document Frequency (IDF) / Frequency-based feature selection
• ISOMAP
  • using / Implementation of SVD using R
  • geodesic distance approximation, calculating / Implementation of SVD using R

J

• JavaScript Object Notation (JSON) / JSON
• Java Virtual Machine (JVM) / Training a model with new features
• joint distribution / Joint distribution

K

• k-fold cross-validation / k-Fold
• kernel functions / Kernel Trick
• Kernel Trick / Kernel Trick
• kernlab
  • implementations / Kernel Trick
  • reference / Kernel Trick
• koRpus package / koRpus

L

• L-BFGS
  • about / Maxent implemenation in R
• language detection
  • about / Language detection
• language models
  • about / Language models
  • N-gram models / N-gram models
  • Markov assumption / Markov assumption
  • hidden Markov models / Hidden Markov models
• language package
  • about / languageR
• languageR package / Lexical richness
• Latent Dirichlet Allocation (LDA) / Latent Dirichlet Allocation
• Latent Semantic Analysis (LSA)
  • about / Latent semantic analysis
  • R Package / R Package for latent semantic analysis
  • example / Illustrative example of LSA
• learning curve
  • about / Learning curve
• leave-one-out method / Leave-one-out
• lemma / Word tokenization
• lexical diversity
  • about / Lexical diversity
  • analyse lexical diversity / Analyse lexical diversity
  • calculating / Calculate lexical diversity
  • readability / Readability
  • automated readability index / Readability
• lexical richness
  • about / Lexical richness
  • lexical variation / Lexical variation
  • lexical density / Lexical density
  • lexical originality / Lexical originality
  • lexical sophistication / Lexical sophistication
• linear kernel
  • applying / How to apply SVM on a real world example?
• linguistics
  • quantitative methods / Quantitative methods in linguistics
• lsa package / lsa

M

• Maxent package
  • implementing, in R / Maxent implemenation in R
• maxent package / maxent
• maximum entropy classifiers / Number of instances is significantly larger than the number of dimensions.Maximum entropy classifier
• model evaluation
  • about / Model evaluation
  • confusion matrix / Confusion matrix
  • ROC curve / ROC curve
  • precision-recall / Precision-recall
• model files
  • reference / OpenNLP
• model validation methods
  • leave-one-out / Leave-one-out
  • k-fold cross-validation / k-Fold
  • bootstrapping methods / Bootstrap
  • stratified sampling / Stratified
• multi-word expressions (MWE) / Collocation and contingency tables
• multi-word units (MWU) / Collocation and contingency tables
• MySQL software
  • download link / Databases

N

• n-fold cross-validation / Leave-one-out
• named entity recognition
  • about / Named entity recognition
  • model, training with new features / Training a model with new features
• natural language processing (NLP) / Collocation and contingency tables

O

• occurrences
  • counting / Counting occurrences
• ODBC Bridge
  • download link / Databases
• OpenNLPmodels.language package
  • installation link / OpenNLP
• OpenNLP package / OpenNLP
• operations, on document-term matrix
  • frequent terms / Operations on a document-term matrix
  • term association / Operations on a document-term matrix
• OWLQN
  • about / Maxent implemenation in R

P

• part-of-speech (POS) / N-gram models
• pointwise mutual information (PMI) / N-gram models
• poisson distribution / Poisson distribution
• POS tagging
  • Hidden Markov Models (HMM) / Hidden Markov Models for POS tagging
• pre-trained POS models, for OpenNLP
  • reference / POS tagging with R packages
• pre-trained sentence boundary detection models
  • reference / Sentence boundary detection
• precision-recall
  • about / Precision-recall
• precompiled binaries
  • download link / PDF documents
• principal component analysis (PCA)
  • about / Principal component analysis
  • R, using / Using R for PCA
• probability
  • about / Probability theory and basic statistics
  • space / Probability space and event
• probability distributions
  • R, using / Probability distributions using R
• probability frequency function / Probability frequency function

Q

• quantitative methods, linguistics
  • about / Quantitative methods in linguistics
  • document term matrix / Document term matrix

R

• R
  • using, for probability distributions / Probability distributions using R
  • used, for singular vector decomposition (SVD) implementation / Implementation of SVD using R
• R, using for principal component analysis (PCA)
  • about / Using R for PCA
  • FactoMineR package / Understanding the FactoMineR package
  • Amap package / Amap package
  • proportion of variance / Proportion of variance
  • scree plot function / Scree plot
• random variables
  • about / Random variables
  • discrete random variables / Discrete random variables
• RcmdrPlugin.temis package / RcmdrPlugin.temis
• Rcurl / HTTP
• Receiver Operating Characteristics Curve (ROC)
  • about / ROC curve
• reducible error components
  • dealing with / Dealing with reducible error components
• regular expressions
  • used, for processing text / Processing text using regular expressions
• relation between words, quantifying
  • about / Quantifying the relation between words
  • contingency tables / Contingency tables
  • detailed analysis, on textual collocations / Detailed analysis on textual collocations
• RKEA package / RKEA
• R Package, for topic modeling
  • about / R Package for topic modeling
  • LDA model, fitting with VEM algorithm / Fitting the LDA model with the VEM algorithm
• R packages, text mining
  • OpenNLP / OpenNLP
  • Rweka / Rweka
  • RcmdrPlugin.temis / RcmdrPlugin.temis
  • tm / tm
  • languageR / languageR
  • koRpus / koRpus
  • RKEA / RKEA
  • maxent / maxent
  • lsa / lsa
• R tau package / Counting occurrences
• RTextTools
  • about / RTextTools: a text classification framework
• RWeka package / Rweka

S

• segmentation / Tokenization and segmentation
• sensitivity / Confusion matrix
• sentence / Word tokenization
• sentence boundary detection
  • about / Sentence boundary detection
  • Word token annotator / Word token annotator
• sentence completion feature / Sentence completion
• singular vector decomposition (SVD)
  • about / Multiple correspondence analysis
  • implementing, with R / Implementation of SVD using R
• speech tagging
  • components / Parts of speech tagging
  • POS tagging, with R packages / POS tagging with R packages
• state distribution / Hidden Markov models
• statistics
  • origin / Probability theory and basic statistics
• strata / Stratified
• stratified sampling / Stratified
• Support vector machines (SVM)
  • applying, on real world example / How to apply SVM on a real world example?

T

• table(tags) / POS tagging with R packages
• Term Document Matrix (TDM) / Dimensionality reduction
• term frequency (TF) / Inverse document frequency
• text
  • accessing, from diverse sources / Accessing text from diverse sources
  • accessing, from file system / File system
  • accessing, from databases / Databases
  • processing, with regular expressions / Processing text using regular expressions
  • tokenization / Tokenization and segmentation
• TextCat / Language detection
• text clustering
  • about / Text clustering
  • feature selection / Feature selection for text clustering
• text mining
  • R packages / R packages for text mining
• texts
  • normalizing / Normalizing texts
  • lemmatization / Lemmatization and stemming
  • stemming / Stemming, Lemmatization
  • synonyms / Synonyms
• TF*IDF / Inverse document frequency
• tm package / tm
• tokenization
  • about / Tokenization and segmentation
  • word tokenization / Word tokenization
  • document-term matrix, operations / Operations on a document-term matrix
  • sentence segmentation / Sentence segmentation
• tokens / Word tokenization
• topic models
  • using / Topic modeling
  • Latent Dirichlet Allocation (LDA) / Latent Dirichlet Allocation
  • Correlated topic model (CTM) / Correlated topic model
• types / Word tokenization

U

• utterance / Word tokenization

W

• word form / Word tokenization
• word sense / Word tokenization

Z

• Zipf's law / Zipf's law