Are there any Python NLP tools to figure out how many ways a sentence can be parsed?

Question

I want to be able to measure ambiguity of a sentence, and my current my idea to do so is by measuring how many ways a sentence can be parsed. For example, the sentence "Fruit flies like a banana" can have to interpretations.

So far I have tried using the Stanford Parser, but it only interpreted each sentence in one way. My other idea was to measure how many different parts of speech each word in a sentence could mean, but each POS tagger I found only marked each word with 1 tag even when it could be multiple.

Are there are tools to do either?

Answer 1

From the Stanford Parser FAQ page , hope it helps:

Can I obtain multiple parse trees for a single input sentence?

Yes, for the PCFG parser (only). With a PCFG parser, you can give the option -printPCFGkBest n and it will print the n highest-scoring parses for a sentence. They can be printed either as phrase structure trees or as typed dependencies in the usual way via the -outputFormat option, and each receives a score (log probability). The k best parses are extracted efficiently using the algorithm of Huang and Chiang (2005).