CS 6320.002: Natural Language Processing - IT Assignment Help

Assignment Task:

Task:

Generating Features – 15 points

The first thing we need to do is generate features for our input. Recall that an MEMM is just a multiclass logistic regression. The sequence information is part of the feature vector, not part of the model itself, so a training example is a single word/tag pair, not an entire sentence, and we will write functions to generate features at the word level. For ease of implementation, we are using simple features, not the pairwise features shown in the lecture slides. We break down the features into two types: n-gram features and word property features. The n-gram features capture word sequence information, while the word property features capture the word’s general characteristics.

Fill in the function get ngram features(words, i). The argument words is a list of strings (words), and the argument i is an int indicating the the current word to generate features for. The function should return a list containing the following strings:

• ‘prevbigram-[x]’, where [x] is the previous word wi−1
• ‘nextbigram-[x]’, where [x] is the next word wi+1
• ‘prevskip-[x]’, where [x] is the next previous word wi−2
• ‘nextskip-[x]’, where [x] is the next next word wi+2
• ‘prevtrigram-[x]-[y]’, where [x] is wi−1 and [y] is wi−2
• ‘nexttrigram-[x]-[y]’, where [x] is wi+1 and [y] is wi+2
• ‘centertrigram-[x]-[y]’, where [x] is wi−1 and [y] is wi+1

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