ScholarMatic: Explanation & Answer
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Lab 1.1: Intro to python Lab 1.2: Functions And Sets Lab 2.1: Preprocessing Text, Tokenization, random sampling of sentences Lab 2.2: Normalising, Number and case, stemming & lemmatization, punctuation and stopword removal. Lab 2.3: Regular Expressions Lab 3.1: Basic Document Classification, creating training and testing sets from data, creating bag-of-words representations using FreqDist, creating word lists, creating word list based classifier, using classifier on test data Lab 3.2: Calculating accuracy of a classifier, getting the train and test data, precision, recall, f1 score, graphs to store results Lab 4.1: constructing a Naive Bayes classifier, creates lists, class priors, conditional probability of a document, add one smoothing, known vocabulary, underflow Lab 4.2: Evaluating NB classifier on test data, NLTK nb classifier Lab 6.1: Document similarity, measuring similarity, cosine similarity, beyond frequency, Lab 7.1: Lexical semantics, navigating wordnet, synsets for PoS, distance to roots, semantic similarity in wordnet, resnik and lin similarity scores, scatter plots comparing resnik, lin similarity to human similarity. Lab 8.1: Distributional semantics, most frequent, generating feature representations, PMI, positiver PMI & Vectors, word similarity, nearest neighbor, Lab 9.1: PoS tagging, average PoS tag ambiguity, freqDist of tags for every word in input, Entropy as a measure tag of ambiguity, simple unigram tagger, beyond unigram tagging, hidden markov model tagger Lab 10.1: Named Entity Recognition, SpaCy, make tag lists, extracting entities, Lab 11.1: Info retrieval, Question and answering, SQUAD datatset, keyword search, docsearch, keyword index, ranking documents, tf-idf, Weekly content Complete all items D Week 1: Intro to NLE and Python Mark completed Week 2: Text Documents and Preprocessing Mark completed Week 3: Document classification Mark completed Week 4: Further document classification Mark completed DWeek 5: Consolidation D Week 6: Document Similarity and Clustering Mark completed Week 7: Lexical Semantics and Word Senses Mark completed O D Week 8: Distributional semantics Mark completed D Week 9: Part-of-speech tagging and Hidden Markov Models Mark completed D Week 10: Named entity recognition (NER) and information extraction (IE) Mark completed Week 11: Question answering (QA) Mark completed
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ScholarMatic: Explanation & Answer
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