JUST P COULD EVERYBODY SORT OF START FROM THAT I MEAN I GUESS WE CAN UM IT'S IN WHAT IS IMPLEMENTED IN RAINBOW IS INFORMATION GAIN AND I'M NOT QUITE SURE HOW THEY CALCULATE THAT LIKE 'CAUSE FREQUENCY WOULD BE USEFUL I THINK BUT UM DEPENDING ON THE CONTEXT THE SIZE AND WHAT WE CONSIDER A DOCUMENT IN THE SENSE OF CALCULATING T. F. I. D. F. IS GONNA CHANGE WHICH MIGHT NEED THINKING ABOUT WELL I THINK WE MIGHT HAVE A LOT IN COMMON WHAT WE CALCULATE BECAUSE I FOR MY LATENT SEMANTIC ANALYSIS NEED LIKE COUNTS OF WORDS WITHIN A DOCUMENT UH WITHIN A A SEGMENT ACTUALLY WITHIN A TOPIC SEGMENT UH THAT'S WHAT RAINBOW DOES I THINK YOU J CAN JUST GET PROBABILITIES FOR A CERTAIN WORDS FOR EACH DOCUMENT CERTAIN UM WE WOULD HAVE TO LOOK AT THAT OKAY OH SO THAT'S WHAT F RAINBOW DOES BECAUSE THAT'S WHAT L. S. A. BUILDS ON LIKE IT BUILDS A F A DOCUMENT BY FREQUENCY MATRIX SO I COULD PROBABLY GET THAT EVEN THOUGH BUT I ALREADY HAVE I ALREADY HAVE MY CODE TO BUILD IT UP MYSELF NO DON'T BOTHER I HAVE MY CODE ALREADY UM YEAH SO DAVE YOU SAID YOU NEED THE FREQUENCY COUNTS ACTUALLY FOR PER DOCUMENT WOULD YOU SAY NOT FOR THE WHOLE THING BUT UM YOU ALSO NEED HOW MANY TIMES THINGS OCCUR WITHIN EACH DOCUMENT AND UM WHAT WE CONSIDER A DOCUMENT'S GONNA DEPEND ON OUR CONTEXT I THINK 'CAUSE IF WE'RE LOOKING AT THE WHOLE LOT OF MEETINGS WE'LL CONSIDER EACH MEETING A DOCUMENT IN SORT OF TERMS OF THIS ALGORITHM AND IF WE'RE VIEWING LIKE SAY JUST A SMALL TOPIC SEGMENT YOU MIGHT LOOK AT EVEN EACH UTTERANCE AS A SMALL DOCUMENT IT MORE AND MORE APPEARS TO ME THAT IF WE IF WE SCRAP THE NOTION OF THE MEETING AS AN INDIVIDUAL THING AND SORT OF JU SEE MEETINGS AS AS TOPIC SEGMENTS AND HAVE SORT OF LIKE HIERARCHICAL TOPIC SEGMENTATION INSTEAD THEN IT'S B LIKE A MORE COHERENT FRAMEWORK IT'S GONNA NEED SOME TH TH THOUGHT OF HOW WE ACTUALLY MAYBE IT DOESN'T ACTUALLY MATTER MAYBE IF YOU JUST DO IT ONCE AT THE HIGHEST LEVEL IT IT WILL BE FINE BUT I WAS JUST THINKING IT MIGHT BE DIFFICULT TO CALCULATE THE T. F. I. D. F. OFF LINE FOR ALL THE DIFFERENT LEVELS WE MIGHT WANT 'CAUSE IF WE'RE GONNA ALLOW DISJOINT SEGMENTS FOR EXAMPLE THEN HOW ARE WE GONNA KNOW WHAT'S GONNA BE IN CONTEXT AT ANY GIVEN TIME BUT I SUPPOSE IF YOU JUST DID IT GLOBALLY TREATING A MEETING AS A DOCUMENT IT'D PROBABLY STILL BE WORK OUT FINE BECAUSE YOU'D ONLY BE COMPARING TO ONES WITHIN THE CONTEXT UH I DON'T KNOW I THOUGHT WERE YOU GONNA USE THAT IN THE END THE INFORMATION DENSITY BECAUSE IF WE'RE DOING LIKE I THINK FOR FOR THE INFORMATION DENSITY WE UH WE SHOULD CALCULATE IT ON THE LOWEST LEVEL NOT ON THE HIGHEST BUT LIKE 'CAUSE LIKE UM YEAH FOR EACH WORD OR WHATEVER BUT ACROSS THE WHOLE LOT IS WHAT I MEAN BY HIGHEST LEVEL LIKE ACROSS THE WHOLE CORPUS YEAH BUT W IT DON'T YOU HAVE TO LIKE GO SORT OF LIKE FOR IN A DOCUMENT VERSUS THE WHOLE THING ISN'T THAT HOW IT WORKS THAT YOU C LOOK LOOK AT R I DON'T THINK THAT'S A GOOD IDEA BECAUSE ISN'T IT LIKE THAT WE EXPECT TH THERE TO CHANGE OVER I B WITH THE DIFFERENT TOPIC SEGMENTS MORE THAT THEY TALK ABOUT SOMETHING DIFFERENT IN EACH DIFFERENT TOPIC SEGMENT MM POSSIBLY 'CAUSE THAT'S WHAT RELATIVE TERM FREQUENCY IS ABOUT THAT LIKE IN SOME CONTEXT THEY'RE TALKING MORE ABOUT A CERTAIN WORD THAN IN GENERAL SO THAT WOULD MORE BE THE THE TOPIC SEGMENTS THEN YEAH THAT'S WHAT I THOUGHT AS WELL THAT YOU THAT PROBABLY THE THE TOPIC SEGMENT LEVEL IS THE MOST UM INFORMATIVE FOR THE WORDS YEAH ARE THEY BIG ENOUGH TO GET ANYTHING MEANINGFUL OUT OF I DON'T KNOW SO I'M JUST WONDERING IF THERE'S WAYS TO ABANDON THE WHOLE CONCEPT OF OF MEETINGS AND SORT OF BUT JUST NOT REALLY TREATING SEPARATE MEETINGS AS TOO MUCH OF A SEPARATE ENTITY BUT YOU JUST CONCATENATE AN X. M. L. FILE TOGETHER BUT WE STILL WANT TO HAVE LIKE A NOTION OF MEETINGS FOR THE USER YES DEFINITELY BUT ON ALGORITHMIC LEVEL WHETHER WE ACTUALLY WHETHER THERE'S SOME WAY TO JUST REPRESENT MEETINGS AS AS TOPICS YEAH YOU JUST LIKE WHATEVER YOU WANT TO LOOK AT YOU JUST JAM TOGETHER INTO AN X. M. L. FILE AND THAT'S YOUR MEETING EVEN THOUGH BITS OF IT MAY COME FROM ALL OVER THE PLACE OR WHATEVER I MEAN I DON'T SEE WHY THAT'S REALLY A BIG PROBLEM THAT'S NOT REALLY WHAT I MEANT BUT I THINK I HAVE TO THINK MORE ABOUT WHAT I MEANT UM G I'M CONFUSED ABOUT EVERYTHING SO BASICALLY WHAT YOU'RE SAYING IS YOU CAN TAKE AN ARBITRARY AMOUNT OF DATA AND PROCESS IT WITH THE SAME ALGORITHM IT DOESN'T MATTER CONCEPTUALLY WHAT THAT DATA IS IT COULD BE A MEETING IT COULD BE TWO UTTERANCES IT COULD BE A MEETING PLUS HALF A MEETING FROM SOMEWHERE ELSE I'M I'M NOT SO CONCERNED ABOUT THE M A MEETING PLUS SOMETHING ELSE I'M MORE TALKING ABOUT LIKE YEAH THE KEEPING KEEPING THE SAME ALGORITHM AND THE SAME WAY OF HANDLING IT AND JUST SAYING LIKE JUST THIS THIS TOPIC HERE I UH IT HAPPENS TO BE LIKE A WHOLE MEETING AND IT HAS SORT OF SUB TOPICS SO JUST THAT SORT OF TOPICS A HIERARCHICAL CONCEPT WHERE LIKE A TOPIC WHERE THERE CAN BE SUPER TOPICS AND TOPICS AND THE SUPER TOPICS ARE IN THE END WHAT THE MEETINGS ARE BUT IN GENERAL AT SOME LEVEL SUPER TOPICS ARE TREATED LIKE LIKE TOPICS I DON'T THINK IT'S VERY DIFFICULT THOUGH I MEAN WHAT YOU DO IS YOU JUST BUILD AN X. M. L. FILE AND IF YOU WANT IT TO GET DOWN TO THE UTTERANCES YOU'D GO TO THE LEAVES AND THEN IF YOU WANTED THE NEXT LEVEL UP YOU'D GO TO THE PARENTS OF THOSE AND LIKE JUST GO FROM LIKE THE LEAVES INWARDS TOWARDS THE BRANCH TO BUILD UP THINGS LIKE UM YOU KNOW WHEN YOU CLICK ON A SEGMENT IT'S GONNA HAVE LIKE WORDS OR WHATEVER THAT ARE IMPORTANT HMM MM I'M NOT REALLY SURE WHAT I WANT SO SORRY COULD DESCRIBE THAT AGAIN THE AND LIKE THE UTTERANCES COME OFF OF HERE MAYBE MM-HMM THEN WHEN WHATEVER YOUR ALGORITHM IS DOING AS LONG AS WHEN YOU'RE WORKING WITH UTTERANCES YOU GO FOR ALL THE LEAVES LIKE THEN IF YOU NEED SOMETHING NEXT UP SO LIKE A TOPIC SEGMENT YOU'D GO TO HERE BUT IF YOU WERE LOOKING AT SAY THIS ONE SO ONLY WENT LIKE THIS RIGHT SO YOU IT'S SAME YOU'D START WITH THE LEAVES AND YOU GO OH I WANT A TOPIC SEGMENT SO I GO ONE LAYER UP MM-HMM SEE AND THEN IF YOU'RE WORKING WITH JUST A TOPIC SEGMENT IN THERE IT'S THE ONLY THING YOU HAVE TO WORRY ABOUT