Question

How would this be done with python using NLTK.

The majority of WordNet's senses are marked by five part-of-speech (POS) categories: noun (n), verb (v), adjective (a), adverb (r), and satellite adjectives (s). Provide the percentage of words from the WordNet corpus that have senses in more than one of these categories.
For example, type has senses which connect to both "noun" and "verb" POS (positive case), whereas typewriter has only senses which connect to "noun" POS (negative case).
Note that, for instance, the following code
from nltk.corpus import wordnet
wordnet.synsets("cat","n") will return you all the senses of word "cat" that are nouns.

Answer 1

To find the percentage of words with multiple parts of speech in WordNet using NLTK, iterate through all lemmas, check their parts of speech, and calculate the proportion of those appearing in more than one category. The relevant parts of speech are noun, verb, adjective, adverb, and satellite adjective.

Step-by-step explanation:

To calculate the percentage of words from the WordNet corpus that have senses in more than one part of speech category, you can utilize Python's NLTK library. You would iterate through all the lemmas in WordNet, check the parts of speech for each word, and then determine how many belong to multiple categories.

The five main parts of speech categories in WordNet are noun (n), verb (v), adjective (a), adverb (r), and satellite adjectives (s). You can check each word against these categories to see in how many categories it appears.

Here is a Python code snippet that demonstrates how to achieve this:

from nltk.corpus import wordnet

def count_multi_pos_words():
multi_pos_words = 0
for synset in wordnet.all_synsets():
pos_counts = len(set(lemma.name() for lemma in synset.lemmas()))
if pos_counts > 1:
multi_pos_words += 1
total_words = len(set(wordnet.all_lemma_names()))
return (multi_pos_words / total_words) * 100

percentage = count_multi_pos_words()
print('Percentage of words with multiple POS:', percentage)

This code calculates the percentage of unique words that appear in multiple parts of speech within the WordNet database.