Q$1.$ Lesk Algorithm for Word Sense Disambiguation: Code $[10]$

In this question, you will implementing Simplified Lesk algorithm for Word Sense Disambiguation task.

$1.$ Load SemCor corpus using NLTK $^(1)$ with semcor. sents$().$ Similarly, load WordNet model in NLTK as import wordnet as wn$.$ Randomly select $50$ sentences and store the sentences $($sents$())$ and their corresponding tagged version $($tagged$\_$sents$())$ as data and labels for first $2$ models.

$2.$ Our first model for word sense disambiguation is Most Frequent Sense model, in which, as the name suggests, we choose most frequent sense for each word from the senses in a labelled corpus. For wordnet, this corresponds to the first sense in synset$().$ Using synset $()$ and definition$(),$ find the sense for each word. Evaluate and report the results using precision, recall and F$-$score.

$3.$ Our second model is Simplified$-$Lesk algorithm as follows:

function SIMPLIFIED LESK$($word$,$ sentence$)$ returns best sense of word.

Here, ComputeOverlap method calculates the number of words overlapping in the context

$($sentence$)$ and the definition of the word from wordnet excluding the stopwords. The sense with largest overlap is chosen.

$4.$ Evaluate and report the results with tags from the dataset using precision, recall and F$-$score.