THE ENTROPY CALCULATION IS THAT SHE BOXED IT UNDER THE SIMPLE CALCULATION THAT YOU COULD PROBABLY WRITE THE SCRIPT IN NO TIME AT ALL AND AND IT IT WOULDN'T BE ANY WORK AT ALL IN TERMS OF PROGRAMMING OR SOMETHING I CAN JUST LIKE K K I CAN SIT TOGETHER WITH YOU FOR TWENTY MINUTES AND JUST SHOW YOU THE ENTROPY CODE THAT I WROTE FOR MY OTHER PROJECT AND IT PROBABLY AND WE SHOULD WORK TOGETHER BECAUSE IT'S WE USED T WE HAVE TO WE'D USE THE SAME MATRIX AS I'M USING IN MY LATENT SEMANTIC ANALYSIS YOU'D USE TO CALCULATE ENTROPY SCORES AND THEN WE'D HAVE UM A SCORE WHICH ACTUALLY WHICH WOULD BE THE SAME FOR THE WORD IN EACH POSITION SO IN THAT SENSE IT'S DOING SOMETHING A BIT DIFFERENT AND LIKE W BASICALLY THE SCORE THAT I'M TALKING ABOUT IS A CONDITIONAL ENTROPY SCORE WHICH JUST CHECKS HOW MUCH INFORMATION THE FACT THAT THERE'S ONE WORD TELLS YOU ABOUT WHAT WOULD BE THE NEXT WORD BUT THAT'S A RELATIVELY GOOD MEASURE OF WHETHER THAT'S A VERY SPECIFIC WORD IN WHICH CASE THEY ARE USUALLY WORDS WHICH TELL YOU QUITE A LOT OR A VERY GENERAL WORD WHICH USUALLY DOESN'T TELL YOU QUITE A LOT HOW DOES IT CALCULATE THAT ACTUALLY UM IT IT'S BASICALLY THE IT'S THE STANDARD ENTROPY FORMULA AND YOU SORT OF YOU YOU YES YES IT HAS I MEAN I THINK THE OFFICIAL LE SORT OF THE OFFICIAL DESCRIPTION OF WHAT IT TELLS YOU IS UM HOW MUCH THAT LIKE THE FACT THAT A GIVEN WORD OCCURS TELLS YOU ABOUT WHAT'S THE NEXT WORD'S GONNA BE WHICH DOESN'T SOUND TOO EXCITING BUT IT IT JUST WORKS OUT IN THE WAY THAT WORDS WHICH ARE PROMISCUOUS AND WHICH OCCUR WITH EVERYTHING ALL OVER THE PLACE HAVE VERY LOW A SCORES ON THAT AND ALSO USUALLY END UP BEING THE WORDS WHICH ARE P LEAST LIKE EXPRESSIVE AND L CONTAIN LESS INFORMATION WELL LIKE FUNCTION WORDS AND STUFF YEAH FUNCTION WORDS OR JUST VERY GENERAL NOUNS PR PROBABLY LIKE WHAT WHATEV FOR EXAMPLE THE WORD COMPUTER IN THAT CONTEXT YOU COULD IMAGINE IT TO F LIKE BE IN ALL IN ALL THE CONTEXTS BECAUSE I MEAN THE THE WORDS WE'RE LOOK REALLY LOOKING FOR APPEAR NOT TOO OFTEN AND IF THEY APPEAR FIVE TIMES IN THE MEETING AND THEY HAVE EACH TIME A DIFFERENT YOU KNOW DIFFER SOME DIFFERENT SURROUNDED WORDS PERHAPS WE HAVE NOT ENOUGH DATA UM I WAS I WAS GETTING THAT W ACTUALLY SORRY I WAS GETTING THAT WRONG I WAS GETTING IT FROM WHAT I DID MY PROJECT NOW IN THIS CASE WE WOULD DO IT BY PER MEE WORDS PER MEETING SO YEAH BUT THAT'S THE SA UH ALMOST I THINK SIMILAR TO WHAT I'M DOING BECAUSE WORDS THAT ARE IN EVERY CLASS THAT ARE NOT VERY INFORMATIVE BUT WORDS THAT ARE ONLY IN ONE CLASS ARE ARE VERY INFORMATIVE FOR THAT CLASS YES IT IT'S PROBABLY DOING IT'S PROBABLY DOING QUITE THE SAME THING IN THE END BUT I'M JUST SAYING LIKE WITH THAT THING YOU WOULD EASILY HAVE AN ALGORITHM WHICH AT THE MOMENT PROVIDES YOU FOR EACH WORD WITH A SCORE WHICH WE CAN USE ALRIGHT UM NO UM I WAS I WAS I WAS DESCRIBING THE WRONG THING IN THIS CASE WE WOULDN'T BE DOING HOW MUCH IT TELLS YOU ABOUT ANOTHER WORD IN THIS CASE WE WOULD BE DOING GIVEN THAT YOU KNOW A WORD HOW GOOD IS IT AT PREDICTING FROM WHICH UM SPECIFIC TOPIC THAT WAS YEAH OKAY SO THAT WOULD YEAH IN THAT SENSE IT'S THE SAME THING HERE MM-HMM BUT THAT WOULD GIVE THE SAME VALUE FOR ONE WORD EVERY TIME I MEAN FOR FOR A SPECIFIC WORD YEAH BECAUSE THEN IT WOULD BE QUITE EASY TO RE INTEGRATE IT INTO SUCH A THE SAME WORD THE WORD YESTERDAY WOULD BE WOULD HAVE THE SAME SCORE ALL OVER THE PLACE AND IN MY CASE IT WOULD HAVE I THINK IT WOULD HAVE DIFFERENT UH VALUES FOR EACH CATEGORY BUT IN THAT CATEGORY IT WOULD HAVE THE SAME VALUE AT EACH PLACE DO YOU KNOW WHAT I MEAN OKAY BUT THEN IT WOULD ALSO BE POSSIBLE TO RE INTEGRATE IT IT WOULD BE MORE DIF I MEAN IT WOULD BE A BIT MORE WORK BUT THEN YOU YOU DO WOULDN'T YOU NEED SEVERAL DOCUMENTS FOR EACH CATEGORY OR SEVERAL SEGMENTS FOR EACH CATEGORY DO BUT WILL IT WORD WITHOUT THAT AT ALL UM AT LEAST IT WORKS IF THERE ARE SEVERAL CATEGORIES WITH EACH WITH ONE DOCUMENT EACH BUT IT UM YEAH EVERY S TOPIC WOULD BE ITS OWN CATEGORY AND THE QUESTION IS DOES THE ALGORITHM STILL MAKE ANY SENSE IN THAT I DON'T UNDERSTAND THE ALGORITHM ENOUGH FOR THAT BUT WHAT I'M REALLY LIKE BECAUSE THE ENTROPICAL UM CALCULATION IS SO SIMPLE MAYBE WE SHOULD LOOK INTO MAKING THAT SCORE JUST AS A PRELIMINARY SCORE THAT WE HAVE LIKE IT IT'S A VERY IT GIVES YOU LIKE I'VE LOOKED AT THE RESULT IT GIVES YOU BASICALLY SOMETHING IN THE END WHICH VAGUELY TELLS YOU JUST WHETHER A WORD IS A VERY SPECIFIC WORD OR A VERY GENERAL WORD AND I LIKE THERE IS SOME HOPE THAT PROBABLY HAVING JUST SENTENCES WHERE THERE'S LOTS OF VERY SPECIFIC WORDS IF YOU MARK THEM AS BEING MORE INTERESTING THAN THE WORDS WHICH ARE ONLY VERY GENERAL WORDS THAT THEY WOULD GET US SOMEWHERE UM AND WHAT WELL WHAT SCORE WOULD A WORD GET THAT JUST OCCURS ONCE IN ALL THE CORPUS FOR EXAMPLE PROBABLY ONE POINT ZERO IS VERY HIGH INFORMATION VALUE EVEN THOUGH THERE ARE ALL THE OTHER SAME TOPICS WHERE IT DOESN'T OCCUR YEAH 'CAUSE THIS WOULD YEAH IN IN A SENSE I MEAN THIS IS A BIT LIKE THE WHAT LIKE DOCUMENT FREQUENCY OVER TOTAL FREQUENCY MM-HMM MEASURE IT SORT OF JUST GOING BY HMM SO WOULD IT BE HIGHER SCORED THAN A WORD THAT UM OCCURS IN EVERY UM SEQUENCE OF THE SAME TOPIC WHAT DO YOU MEAN EVERY SEQUENCE OF THE SAME FOR EXAMPLE WE HAVE TWENTY TOPICS AND UM FOR ONE OF THESE TOPICS THERE ARE FIVE OCCURRENCES AND WE HAVE ONE WORD THAT YOU MEAN LIKE JUST LIKE FROM FROM A BEGINNING TO END POINT LIKE WITHIN ONE MEETING THERE ARE SEVERAL TOPICS YES BUT ACROSS MEETINGS UM THERE WILL BE THE SAME TOPIC SEVERAL TIMES BUT WE DON'T HAVE THAT INFORMATION ANYWHERE DO WE AH IT WASN OKAY I THOUGHT THAT WAS WHAT YOU WERE DOING WELL UM WHEN IT SPLITS THE TOPICS UP IT DOES DO IT ON REGULAR WORDS THAT UM THAT OCCUR BUT IT DOESN'T TELL YOU WHAT THEY ARE NO BUT BUT YOU YOU ARE SEGMENTING HMM WELL I'M I'M DOING ONE ON SEGMENT SIMILARITY IN THE END YEAH I'M DOING LIKE FINDING SIMILAR SEGMENTS BASIC LATENT SEMANTIC ANALYSIS BUT BUT LIKE F FOR NOW LIKE YOUR SEGMENTATION IS JUST SPLITTING A MEETING UP INTO DIFFERENT BLOCKS VER NOT NOT FROM WHAT COLIN IS DOING FROM WHAT I NO