Walden University Programming Worksheet Solution Studyhelp247
Description
I have a Computer Science live assignment on the 4th of January at 12 PM EST and it is based on python.The time limit is 3 hours long.I have attached the unitsI have also attached the practice live assignment- which will be very similar to the actual live assignment for January 4th. I have also attached a previous lab we did and the lab description.
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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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