Course materials for Applied Natural Language Processing (Spring 2019). Syllabus: http://people.ischool.berkeley.edu/~dbamman/info256.html

Notebook	Description
1.words/EvaluateTokenizationForSentiment.ipynb	The impact of tokenization choices on sentiment classification.
1.words/ExploreTokenization.ipynb	Different methods for tokenizing texts (whitespace, NLTK, spacy, regex)
1.words/TokenizePrintedBooks.ipynb	Design a better tokenizer for printed books
2.distinctive_terms/ChiSquare.ipynb	Find distinctive terms using the Chi-square test
2.distinctive_terms/CompareCorpora.ipynb	Find distinctive terms using the Mann-Whitney rank sums test
3.dictionaries/DictionaryTimeSeries.ipynb	Plot sentiment over time using human-defined dictionaries
4.classification/CheckData_TODO.ipynb	Gather data for classification
4.classification/FeatureExploration_TODO.ipynb	Feature engineering for text classification
4.classification/FeatureWeights_TODO.ipynb	Analyze feature weights for text classification
4.classification/Hyperparameters_TODO.ipynb	Explore hyperparameter choices on classification accuracy
5.text_regression/Regularization.ipynb	Linear regression with L1/L2 regularization for box office prediction
6.tests/BootstrapConfidenceIntervals.ipynb	Estimate confidence intervals with the bootstrap
6.tests/ParametricTest.ipynb	Hypothesis testing with parametric (normal) tests
6.tests/PermutationTest.ipynb	Hypothesis testing with non-parametric (permutation) tests
7.embeddings/DistributionalSimilarity.ipynb	Explore distributional hypothesis to build high-dimensional, sparse representations for words
7.embeddings/TFIDF.ipynb	Explore distributional hypothesis to build high-dimensional, sparse representations for words (with TF IDF scaling)
7.embeddings/TurneyLittman2003.ipynb	Use word embeddings to implement the method of Turney and Littman (2003) for calculating the semantic orientation of a term defined by proximity to other terms in two polar dictionaries.
7.embeddings/WordEmbeddings.ipynb	Explore word embeddings using Gensim
8.neural/MLP.ipynb	MLP for text classification (keras)
8.neural/ExploreMLP.ipynb	Explore MLP for your data (keras)
8.neural/CNN.ipynb	CNN for text classification (keras)
8.neural/LSTM.ipynb	LSTM for text classification (keras)
8.neural/Attention.ipynb	Attention over word embeddings for document classification (keras)
8.neural/AttentionLSTM.ipynb	Attention over LSTM output for text classification (keras)
9.annotation/IAAMetrics.ipynb	Calculate inter-annotator agreement (Cohen's kappa, Krippendorff's alpha)
10.wordnet/ExploreWordNet.ipynb	Explore WordNet synsets with a simple method for finding in a text all mentions of all hyponyms of a given node in the WordNet hierarchy (e.g., finding all buildings in a text).
10.wordnet/Lesk.ipynb	Implement the Lesk algorithm for WSD using word embeddings
10.wordnet/Retrofitting.ipynb	Explore retrofit word vectors
11.pos/KeyphraseExtraction.ipynb	Keyphrase extraction with tf-idf and POS filtering
11.pos/POS_tagging.ipynb	Understand the Penn Treebank POS tags through tagged texts
12.ner/ExtractingSocialNetworks.ipynb	Extract social networks from literary texts
12.ner/SequenceLabelingBiLSTM.ipynb	BiLSTM + sequence labeling for Twitter NER
12.ner/ToponymResolution.ipynb	Extract place names from text, geolocate them and visualize on map
13.mwe/JustesonKatz95.ipynb	Implement Justeson and Katz (1995) for identifying MWEs using POS tag patterns
14.syntax/SyntacticRelations.ipynb	Explore dependency parsing by identifying the actions and objects that are characteristically associated with male and female characters.
15.coref/CorefSetup.ipynb	Install neuralcoref for coreference resolution
15.coref/ExtractTimeline.ipynb	Use coreference resolution for the task of timeline generation: for a given biography on Wikipedia, can you extract all of the events associated with the people mentioned and create one timeline for each person?
16.ie/DependencyPatterns.ipynb	Measuring common dependency paths between two entities that hold a given relation to each other
16.ie/EntityLinking.ipynb	Explore named entity disambiguation and entity linking to Wikipedia pages.
17.clustering/TopicModeling_TODO.ipynb	Explore topic modeling to discover broad themes in a collection of movie summaries.