THERE'S A LOT OF INFORMATION THAT'S COMPLETELY LOST IN THIS BAG OF WORDS REPRESENTATION AND I WAS HOPING THAT IN SOME WAY YOU COULD USE THAT TO EVALUATE THIS I HAVE NO IDEA HOW BUT UH YEAH UH I DON'T GET IT EXACTLY WHAT YOU MEAN THEN DON'T DON'T USE A GOOGLE PAGE BECAUSE IT'S UH TOO UH S ROOT BUT UH ANOTHER PAGE MM SOMEONE WOULD SAY IT'S A IT'S A IT'S A IT'S A IT'S A WEB PAGE UH WH WHICH HAS VERY FEW UH INFORMATION IN IT OR ANYTHING SO I THINK THERE'S NO HARD CLUSTERING AND WHAT'S GOOD WITH THOSE MODEL IS THAT THEY THEY CAN THEY THEY ARE INTENDED TO PROVIDE YOU SOME SOME KIND OF WAY TO COMPARE TWO DOCUMENTS RATHER THAN TO GIVE YOU PRE DEFINED SETS OF DOCUMENTS THIS IS NOT HARD CLUSTERING THE O I I DON'T THINK ONE ONE ONE ASPECT OR ONE LATENT TOPIC HAS ANY SENSE BY HIS OWN IT'S WHEN YOU HAVE THE WORD SET AND THE WORD DISTRIBUTION THAT YOU C YOU CAN TELL SOMETHING IF YOU JUST TAKE ONE OF THEM ALONE SO YOU TAKE ALL THE P. OF Z. GIVEN D. AND ALL OF THE P. OF W. GIVEN GIV GIVEN Z. YOU YOU YOU CAN DO ANYTHING WITH THAT IT'S IT'S THE WHOLE SET I WOULD SAY AN AN AND THIS IS OH IT IT SHOULD BE EVALUATED MEANING THAT LIKE FOR EXAMPLE UH THE THE EXAMPLE ABOUT MARBLES IS THAT THE POINT WHAT YOU CAN LEARN FROM ALL THESE SETS MADE BY PEOPLE IS THAT IS THAT THOSE TWO THOSE TWO MARBLE WOULD BE CONSIDER AS MORE LIKELY TO BE IN THE SAME SETS BECAUSE THEY THEY SHARE SOME COMMON PROPERTIES WHICH ARE NOT SEEN AS THE SAME FOR EVERY ONE BUT THEY SHARE SOME COMMON PROPERTIES THAT PEOPLE WILL NOTICE AND IF YOU LOOK AT THE TWO THE D ALL POSSIBLE PAIRS OF MARBLE YOU WOULD HAVE SOME MU MUCH MORE AGREEMENT BETWEEN PEOPLE THAN THAN IF YOU LOOK AT THE PREDEFINED SETS PEOPLE WOULD WOULD JUST CUT AT SOME PRECISE UH POINT YOU KNOW WHAT'S OKAY I THINK I KNOW IT BUT IT YEAH THI THIS IS EFFECTIVELY WHAT WE HAVE HERE THOUGH AND TH THAT DIRECTORY IS ONE CLUSTERING OF THE MARBLES AND YOU'RE HOPING THAT IT IT SO WHAT YOU'RE SAYING IS THAT I HAVE TWO CLUSTERINGS OF MARBLES FOR MY TWO MODELS AND I HAVE A TEST CLUSTERING OF MARBLES IT'S SAYING THAT ONE OF THESE IS MORE CLOSE TO THE TEST T T TO THE THE ONE THAT WE'VE BEEN GIVEN IS THAT IT'S SAYING THAT WHAT DOES THAT ACTUALLY SAY THAT DOESN'T UH IT SAYS IT'S G BETTER FOR THIS TASK BUT IT DOESN'T REALLY SAY ANYTHING ELSE WHAT YOU HAVE IS Y IS YOU HAVE IS YOU HAVE ONE CLUSTERING OF MARBLE WHICH IS THIS ONE AND THEN YOU HAVE LOTS OF PEOPLE TELLING YOU I CONSIDER THOSE MARBLES AS VERY UH HAVE TO HAVE VERY SALIENT FEATURE THAT THEY SHARE IN COMMON I I C I WOULD LIKE THEM TO BE IN THE SAME CLUSTER AND YOU C AND AND AND THOSE TWO SO AND ONE OF THEM IS LABELLED AND THE OTHER IS NOT LABELLED SO YOU LOOK AT THAT YOU SAY OH SOMEONE TOLD THIS ONE WAS VERY SIMILAR AND THIS ONE IS SAID TO BE BLUE FOR EXAMPLE I DUNNO IF IT'S THE LABEL SO I PUT IT IN THE BLUE BAG OKAY YOU JUST LOOK AT THE PEOPLE YOU YOU JUST HAVE THE PEOPLE TO COMPARE THOSE TWO MARBLES AND THEN IT HAPPENS THAT SOME OF THEM HAVE BEEN CLUSTERED AND AND THAT'S THE POINT MM IT'S GETTING OKAY UH AND SO AND SO IF I GET THAT RIGHT WHAT WHAT THE ALGORITHMS ARE STILL COMING UP WITH IS IN THIS WAY YOU'RE LOOKING AT A CLUSTERING OF MARBLES YEAH IF EACH OF THE ALGORITHMS COMES UP WITH A DEFIN IT COMES UP WITH NOT WITH THE CLUSTERING BUT WITH SOME WAY TO COMPARE TWO MARBLES OKAY SOME WOULD SAY WOULD SAY I CONSIDER THOSE TWO ONES ARE COMPLETELY DISSIMILAR AND THE OTHER ONES SAY THEY ARE VERY SIMILAR AND IF YOU FIND THAT IN YOUR UNL UNLABELLED CLUSTERING THOSE TWO UH BALLS UH MARBLES HAPPEN TO BE IN THE SAME UH CLUSTER YOU SAID THE USER ALGORITHM MIGHT BE WRONG BUT YOU AVERAGE THAT OVER LOTS OF DOCUMENTS OKAY BECAUSE THE THE LATENT ASPECTS REALLY DOESN'T HAVE TO MATCH THE FINAL UH DIRECTORY CATEGORY IT CAN BE ANYTHING BUT IT'S JUST A WAY OF SAYING YEAH THOSE DOCUMEN DOCUMENTS ARE SIMILAR IN SOME SENSE SO WE'RE NOT TRYING TO TO FIT YEAH OKAY I'M CONVINCED I'M CONVINCED ALRIGHT SO IT'S GOOD RIGHT UH ALRIGHT WELL THANKS UM I'M TRYING TO THINK OF ANYTHING I HAVEN'T SAID ABOUT THE STUFF BUT THAT UH FROM LOOKING AT THE RESULTS I GOT IT LOOKED QUITE GOOD UM JUST LIKE HAND READING THEM IN THAT ONE OF THE THINGS I DID WAS I STARTED A I A WEB CRAWL AT THE IDIAP HOMEPAGE IT DID NOT VERY MANY PAGES ONLY LIKE TWO THOUSAND OR SOMETHING LIKE THAT BUT ENOUGH TO GET THE PAGES THAT WERE CLOSE TO IDIAP TO HAVE A LOT OF LINKS BETWEEN THEM SO THAT THERE WAS ENOUGH TO BE ABLE TO RUN THIS LINK CLUSTERING ALGORITHM SO WHAT YOU FIND IS THAT THE PAGES AROUND IDIAP THERE'S A LOT OF ENGLISH PA SPEAKING PAGES AND A LOT OF FRENCH SPEAKING PAGES SO OF COURSE THIS UH P. L. S. A. COMPLETELY SEPARATED THOSE OUT INTO INTO CATEGORIES UH SO WHEN I LOOKED AT THE THE WORD LIKELIHOODS THERE WERE TWO TOPICS ONE WAS VERY CLEARLY ENGLISH WORDS THAT MOST LIKELY WORD WAS THE AND ONE WAS VERY CLEARLY FRENCH WORDS MOST LIKELY WORDS WAS DE I THINK AND UH YOU HAD ONL TWO TOPICS OR NO YEAH YEAH WHEN YOU LOOK AT IT YOU CAN TELL VERY CLEARLY THAT ONE OR TWO TOPICS ARE A B NOT THE REST YEAH YEAH YEAH THEY MAKE SENSE ONLY SO GLOBAL OPTIMISATION THE LIKELIHOOD IS OPTIMISABLE GLOBALLY OVER THE WHOLE ASPECTS ETCETERA SO IT'S IT'S YOU NEVER ASK THE MODEL TO DO HARD CLUSTERING OF DOCUMENTS YOU JUST UH ASK UH IT TO FIND A DISTRIBUTION OVER ASPECTS SUCH THAT A DOCUMENT UH IS MORE LIKELY ACCORDING TO THE RESTRICTION THAT THE NUMBER ASPE ASPECT IS LIMITED AND AND SO IT THE IT JUST MAKES SENSE ON THE UH LOOKING AT THE WHOLE SET SO Y I THINK ALL EVALUATION SHOULD TAKE THAT INTO ACCOUNT AND LOOK AT THE WHOLE ASPECT SET LIKE HERE YOU LOOK AT THE ASPECT SET BECAUSE YOU LOOK AT THE SIMILARITY OF TWO DISTRIBUTION AN ANY ST ANY KIND OF THING YOU WHERE YOU UN UNFOLD THE MODEL AND LOOK AT IT MANUALLY LOOKS WEIRD LIKE LIKE IN THOSE HOFFMANN PAPERS WHERE SHOW YOU COLUMNS WITH WORDS OR THINGS HMM THAT W UH ANYWAY THAT WHEN I RUN IT ON THAT UH WHAT I FOUND WAS LOOKING THROUGH SOME OF THE SITES UH I LOOKED FOR THE SITES THAT THE MODELS DIFFERED ON