Python Pandas NLTK: Adding Frequency Counts or Importance Scoring to Part of Speech Chunks on Dataframe Text Column

I did NLTK part of speech tagging followed by chunking on one column (“train_text”) inside my Pandas data frame.

Below is my code that ran successfully and sample output results.

def process_content():     try:         for i in train_text:             words = nltk.word_tokenize(i)             tagged = nltk.pos_tag(words)             # chunkGram = r"""Chunk: {<RB.?>*<VB.?>*<NNP>+<NN>?}"""             chunkGram = r"""Chunk: {<VB.?><NN.?>}"""             chunkParser = nltk.RegexpParser(chunkGram)             chunked = chunkParser.parse(tagged)              for subtree in chunked.subtrees(filter = lambda t: t.label() == 'Chunk'):                 print (subtree)      except Exception as e:         print(str(e))  process_content() 

Results: “xxx” stands for a word; in the first instance it is a verb and in the second instance it is a noun

(Chunk xxx/VBN xxx/NN)  (Chunk xxx/VBN xxx/NN)  (Chunk xxx/VBN xxx/NN)  (Chunk xxx/VBN xxx/NN)  (Chunk xxx/VBN xxx/NN)  

Now that I have the chunks of words, I want to find the 10 most frequently occurring or prominent Verb + Noun chunks. Is there any way I can attach a frequency or importance score to each chunk?