Index
A
- advanced topic modeling
- training tips / Advanced training tips
- documents, exploring / Exploring documents
- Artificial Intelligence Markup Language / ChatBots
B
- bag-of-words model / Bag-of-words, What are topic models?
- bi-grams / n-grams and some more preprocessing
- Brown Corpus / Where's the data at?
- Business Intelligence (BI) / What is text analysis?
C
- Caffe
- reference / Generating text
- Caffe2
- reference / Generating text
- ChatBots / ChatBots
- classification
- about / Clustering text
- with Keras / Classification with Keras
- with spaCy / Classification with spaCy
- clustering
- about / Clustering text
- starting / Starting clustering
- Computational Linguistics (CL) / What is text analysis?
- Constituency Parsing / Dependency parsing
- Continuous Bag of Words model (CBOW) / Word2Vec
- Convolutional Neural Networks (CNNs) / Why should you do text analysis?
- CRFs (Conditional Random Fields) / What is NER-tagging?, NER-tagging in Python
D
- deep learning
- about / Why should you do text analysis?, Deep learning
- for text / Deep learning for text (and more)
- dendrogram / Visualizing topic models
- dependency parsers
- training / Training our dependency parsers
- dependency parsing
- about / What is POS-tagging?, Dependency parsing
- visualizing / Dependency parsing
- in Python / Dependency parsing in Python
- with spaCy / Dependency parsing with spaCy
- Digital Humanities labs / Where's the data at?
- Doc2Vec / Doc2Vec
- Dynamic Topic Modeling / What are topic models?
- dynamic topic models / Dynamic topic models
F
- Facebook Artificial Intelligence Research (FAIR) / Generating text
- FastText / FastText
G
- Garbage in, garbage out (GIGO) / Garbage in, garbage out
- gensim
- about / Introducing Gensim
- primary features / Introducing Gensim
- vector transformations / Vector transformations in Gensim
- topic models / Topic models in Gensim
- Word2Vec, using with / Using Word2Vec with Gensim
- GloVe embeddings / Other word embeddings, GloVe
H
- hierarchical clustering / Hierarchical clustering
- Hierarchical Dirichlet process (HDP) / Hierarchical Dirichlet process
I
- Industrial Strength Natural Language Processing / spaCy
- Information Retrieval (IR) / What is text analysis?
K
- K-means / K-means
- Keras
- reference / Generating text
- about / Keras and spaCy
- Kullback-Leibler divergence / Topic models in scikit-learn
L
- Language Translation / Deep learning for text (and more)
- Latent Dirichlet allocation / Latent Dirichlet allocation
- Latent Semantic Indexing (LSI) / Introducing Gensim, Latent semantic indexing
- LDA algorithm
- hyperparameters / Advanced training tips
- Long Short Term Memory (LSTM) / Generating text
M
- Machine Learning (ML) / What is text analysis?
- MZ keyword extraction algorithm / Summarizing text
N
- n-grams / n-grams and some more preprocessing
- Naive Bayes Classifier / Classifying text
- named entity / What is NER-tagging?
- Named Entity Disambiguation (NED) / What is NER-tagging?
- Named Entity Recognition (NER) / Where's the data at?, Named entity recognition, What is NER-tagging?
- Natural Language Generation (NLG) / Deep learning for text (and more)
- Natural Language Processing (NLP) / What is text analysis?
- NER-taggers
- training / Training our own NER-taggers
- NER-tagging
- about / What is NER-tagging?
- in Python / NER-tagging in Python
- with spaCy / NER-tagging with spaCy
- examples / NER-tagging examples and visualization
- visualization / NER-tagging examples and visualization
- Neural Conversational model / ChatBots
- Neural Machine Translation
- about / What is text analysis?
- example / What is text analysis?
- neural networks
- about / Deep learning
- structure / Deep learning
- Neural Translation model / Deep learning for text (and more)
- Non-negative Matrix Factorization (NMF) / Topic models in scikit-learn
- noun phrase (NP) / Dependency parsing
P
- Part-Of-Speech (POS) tagging / Where's the data at?
- Poincare / Poincare
- Poincare Embeddings / Other word embeddings
- POS-taggers
- training / Training our own POS-taggers
- POS-tagging
- about / Part-of-speech (POS) – tagging, What is POS-tagging?
- in Python / POS-tagging in Python
- with spaCy / POS-tagging with spaCy
- code examples / POS-tagging code examples
- Project Gutenberg / Where's the data at?
- Python
- need for / Why Python?
- text manipulation / Text manipulation in Python
- POS-tagging / POS-tagging in Python
- dependency parsing / Dependency parsing in Python
- PyTorch
- reference / Generating text
R
- Recurrent Neural Networks (RNNs) / Why should you do text analysis?
- Reddit
- for data mining / Reddit for mining data
- reference / Reddit for mining data
- rule-based matching / Rule-based matching
S
- sci-kit learn
- topic models / Topic models in scikit-learn
- sentiment analysis
- about / Deep learning for text (and more), Sentiment analysis
- Reddit, for mining data / Reddit for mining data
- Twitter, for mining data / Twitter for mining data
- shallow learning / Deep learning
- similarity metrics / Similarity metrics
- similarity queries / Similarity queries
- Skip Gram model / Word2Vec
- spaCy
- about / spaCy, Keras and spaCy
- installing / Installation
- troubleshooting / Troubleshooting
- language models / Language models
- language models, installing / Installing language models
- basic pre-processing, with language models / Basic preprocessing with language models
- POS-tagging / POS-tagging with spaCy
- NER-tagging / NER-tagging with spaCy
- dependency parsing / Dependency parsing with spaCy
- Stanford Dependency Parser / Dependency parsing in Python
- stop words / Garbage in, garbage out
- style analysis / Where's the data at?
- Support Vector Machine (SVM) / Classification with Keras
T
- Tensorflow
- reference / Generating text
- tensorizer / Part-of-speech (POS) – tagging
- text
- tokenizing / Tokenizing text
- rule-based matching / Rule-based matching
- pre-processing / Preprocessing
- clustering / Clustering text
- classifying / Classifying text
- summarizing / Summarizing text
- generating / Generating text
- text analysis / What is text analysis?, Why should you do text analysis?
- Text Analysis API
- reference / What is text analysis?
- text corpus / Where's the data at?
- text dataset list
- example / Where's the data at?
- text manipulation, Python / Text manipulation in Python
- TextRank algorithm / Summarizing text
- TF-IDF (term frequency-inverse document frequency) / Introducing Gensim, TF-IDF
- Theano
- reference / Generating text
- tokenization / Tokenizing text
- topic coherence / Topic coherence and evaluating topic models
- topic models
- about / What are topic models?
- dynamic topic models / Dynamic topic models
- in scikit-learn / Topic models in scikit-learn
- evaluating / Topic coherence and evaluating topic models
- visualizing / Visualizing topic models
- topic models, in gensim
- about / Topic models in Gensim
- Latent Dirichlet allocation / Latent Dirichlet allocation
- Latent Semantic Indexing (LSI) / Latent semantic indexing
- Twitter
- for data mining / Twitter for mining data
V
- Varembed / Varembed
- Variational Bayes sampling / Exploring documents
- vectors
- need for / Vectors and why we need them
- vector transformations
- in Gensim / Vector transformations in Gensim
- verb phrase (VP) / Dependency parsing
W
- Word2Vec
- about / Word2Vec
- working / Word2Vec
- using, with gensim / Using Word2Vec with Gensim
- word embeddings
- WordRank / WordRank
