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alignment
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Conversational alignment
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alignment
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# alignment Conversational alignment Code examples - Reading in a csv https://docs.python.org/2/library/csv.html - Grouping list of dictionaries https://www.daniweb.com/software-development/python/code/216750/group-a-list-of-dictionaries-python Formulas - TRUE_POWER (Outputs the expected results) - DNM (Formula used in [Echoes of Power](http://www.cs.cornell.edu/~cristian/Echoes_of_power_files/echoes_of_power.pdf) ## changelog **6/26/2015** Derived formula for Echoes of Power: powerProb = ((# of times marker is said by A and B)*(# of words A says to B))/((# of words B says to A)*(# of times A says the marker)) baseProb = (# of times B says the marker)/(# of words B says) prob = powerProb - baseProb **6/29/2015** Figured out that the formula we were working off of was incorrect. Instead of calculating probabilities using tokens, we need to calculate probabilites using utterances powerProb = (# of utterances where A and B both say marker)/(Number of utterances where A says the marker) baseProb = (# of utterances where B says the marker)/(# of utterances that B says to A) prob = powerProb - baseProb **7/13/2015** Figured out that the formula we were working off of was incorrect. Our new formula is: powerProb = log((# of utterances where A and B both say marker)/(Number of utterances where A says the marker)) baseProb = log((# of utterances where B says the marker and A doesn't)/(# of utterances that A doesn't say the marker)) prob = powerProb - baseProb **7/20/2015** SHARED_CODE: Fixed a bug which was reducing our BNOTA **7/22/2015** SHARED_CODE: Added DNM formula option and options to change smoothing values **7/23/2015** TWTR: Updated readCSV to discard more utterances which quote a speaker - Check for unicode quotes - Check if speakers acknowledge each other - Check if utterance contains "RT" - Check if utterance contains "[mention]: " **7/27/2015** - No longer discard if speakers don't acknowledge each other