anlp19

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.

Name With Owner	dbamman/anlp19
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Created At	2019-01-20 16:34:11
Pushed At	2022-02-02 17:56:21
Last Commit At	2021-11-18 09:58:04
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