Homework 6 – Writing python functions and investigating semantic relations with the NLTK

In this homework you will practice writing your own functions to extract
information from various corpora. Please put your answers in an executable python
script named hmwk6.py, and commit it to the subversion repository. Don’t forget to use normal division.
It is due Oct. 9th and covers material up to Oct. 1st.

1. Create a function called mean_word_len, which accepts a list of words (e.g. text1 — Moby Dick), and returns the mean characters per word. You should remove punctuation and stopwords from the calculation. (10 points)
2. Create a function called mean_sent_len, which accepts a list of sentences, and returns the mean words per sentence. You should remove punctuation and stopwords from the calculation. Note that the NLTK .sents() method returns a list of lists. That is, each item in the list represents a sentence, which itself is composed of a list of words. (15 points)
3. Now use your two new functions to print out the mean sentence length and the mean word length for all of the texts from the gutenberg project included in the NLTK. You should print out these statistics with one file per line, with the fileid first, and then the mean word length and sentence length. One example would be:
   melville-moby_dick.txt 5.64357969913 9.79009882174
   (10 points)
4. Using the CMU pronouncing dictionary, create a list of all words which have 3 letters, and 2 syllables. Your final list should include just the spelling of the words. To calculate the number of syllables, use the number of vowels in the word (every vowel includes the digit 1, 2, or 0, marking primary, secondary, or no stress). (15 points)
5. Imagine you are writing a play, and you are you thinking of interesting places to stage a scene. You would like it be somewhere like a house, but not exactly. Use the wordnet corpus to help you brainstorm for possible locations. First, find the hypernyms of the first definition of the word house. Then find all the hyponyms of those hypernyms, and print out the names of the words. Your output should contain one synset per line, with first the synset name, and then all of the lemma_names for that synset, e.g.:
   lodge.n.05 - lodge, indian_lodge
   (10 points)