SemCor is a widely used corpus in supervised learning for Word Sense Disambiguation (WSD), where each sentence contains sense tags. Therefore, it is easy to classify the target word according to its senses. However, I am facing difficulties in extracting the sense key of a particular word and adding it to a list. How can I accomplish this?" **
nltk.download('semcor')
# get SemCor tagged sentences
def lemma_list(sent):
return [l.label() if isinstance(l, nltk.tree.Tree) else None for l in sent]
# get SemCor tagged sentences
semcor_sents = semcor.tagged_sents(tag='sem')
# call lemma_list() on the first sentence
first_sent = semcor_sents[0]
lemmas = lemma_list(first_sent)
print(lemmas)
``'**
From this code, I got only a list containing, for each token of the sentence, the corresponding WordNet lemma. After this, I need to extract the sense_name or sense_id of a particular word (eg, bank) with respect to each context in semcor sentences and append it to a list.