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Course materials for Applied Natural Language Processing (Spring 2019). Syllabus:

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? Measuring common dependency paths between two entities that hold a given relation to each other 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.


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