NLP algorithm to 'fill out' search terms?

Sounds like you want to determine the collocations that Dolphin occurs in. There are various methods for collocation finding, the most popular being to compute point-wise mutual information (PMI) between terms in your corpus, then select the terms with the highest PMI for Dolphin You might remember PMI from the sentiment analysis algorithm that I suggested earlier.

It should basically figure out that if "Dolphin" appears at all, it's virtually always either in the bigrams "Dolphin Watching" or "Miami Dolphins". Sounds like you want to determine the collocations that Dolphin occurs in. There are various methods for collocation finding, the most popular being to compute point-wise mutual information (PMI) between terms in your corpus, then select the terms with the highest PMI for Dolphin.

You might remember PMI from the sentiment analysis algorithm that I suggested earlier. A Python implementation of various collocation finding methods is included in NLTK as nltk.collocations. The area is covered in some depth in Manning and Schütze's FSNLP (1999, but still current for this topic).

I used the Natural Language Toolkit in my NLP class in university with decent success. I think it's got some taggers that can help you determine which are the nouns, and help you parse it into a tree. I don't remember much, but I'd start there.

I cant really gove you an answer,but what I can give you is a way to a solution, that is you have to find the anglde that you relate to or peaks your interest. A good paper is one that people get drawn into because it reaches them ln some way.As for me WW11 to me, I think of the holocaust and the effect it had on the survivors, their families and those who stood by and did nothing until it was too late.