Meeting transcript:
speaker: A
text: Why ?
speaker: E
text: I 'm known . I <disfmarker>
speaker: D
text: Um .
speaker: A
text: No , cuz she already told me it , before she told you .
speaker: E
text: No , she told me a long time ago . She told me <disfmarker> she told me like two weeks ago .
speaker: A
text: Oh , well , it doesn't matter what time .
speaker: B
text: OK .
speaker: B
text: You know how to toggle the display width <pause> function <disfmarker>
speaker: D
text: Wow .
speaker: A
text: Well maybe she hadn't just started transcribing me yet .
speaker: A
text: Anyway .
speaker: D
text: What is it ?
speaker: E
text: Let me explain something to you .
speaker: D
text: Um ,
speaker: E
text: My laugh is better than yours .
speaker: D
text: there .
speaker: B
text: Yo .
speaker: A
text: I beg to differ .
speaker: D
text: Um , OK .
speaker: A
text: But you have to say something genuinely funny before you 'll get an example .
speaker: E
text: Yeah .
speaker: D
text: The thing is I don't know how to get to the next page . Here .
speaker: E
text: No .
speaker: E
text: You should be <disfmarker> at least be self - satisfied enough to laugh at your own jokes .
speaker: D
text: Actually I thought <disfmarker>
speaker: D
text: There .
speaker: A
text: No , it 's a different laugh .
speaker: D
text: How weird .
speaker: A
text: Ooh , wow !
speaker: E
text: Oh ! Holy mackerel .
speaker: A
text: Wow . Whoa !
speaker: D
text: What ? !
speaker: D
text: Oh .
speaker: D
text: OK . I
speaker: D
text: wasn't even doing anything . <vocalsound> OK .
speaker: A
text: Uh .
speaker: E
text: Eva 's got a laptop , she 's trying to show it off .
speaker: D
text: That was r actually Robert 's idea . But anyhow .
speaker: D
text: Um
speaker: F
text: O K . So , here we are .
speaker: E
text: Once again .
speaker: F
text: Once again ,
speaker: F
text: right ,
speaker: F
text: together .
speaker: F
text: Um , so we haven't had a meeting for a while ,
speaker: F
text: and <disfmarker> and probably won't have one next week ,
speaker: F
text: I think a number of people are gone .
speaker: F
text: Um , so Robert , why don't you bring us up to date on where we are with EDU ?
speaker: B
text: Um ,
speaker: B
text: uh in a <disfmarker> in a smaller group we had uh , talked and decided about continuation of the data collection .
speaker: B
text: So Fey 's time with us is almost officially over ,
speaker: B
text: and she brought us some thirty subjects and , t collected the data ,
speaker: B
text: and ten dialogues have been transcribed
speaker: B
text: and can be looked at .
speaker: B
text: If you 're interested in that , talk to me .
speaker: B
text: Um , and we found another uh , cogsci student who 's interested in playing wizard for us .
speaker: B
text: Here we 're gonna make it a little bit more complicated for the subjects , uh this round .
speaker: B
text: She 's actually suggested to look um , at the psychology department students ,
speaker: B
text: because they have to partake in two experiments in order to fulfill some requirements .
speaker: B
text: So they have to be subjected , <vocalsound> <comment> before they can actually graduate .
speaker: B
text: And um ,
speaker: B
text: we want to design it so that they really have to think about having some time ,
speaker: B
text: two days , for example ,
speaker: B
text: to plan certain things and figure out which can be done at what time ,
speaker: B
text: and ,
speaker: B
text: um , sort of package the whole thing in a <disfmarker> in a re in a few more complicated um , structure .
speaker: B
text: That 's for the data collection .
speaker: B
text: As for SmartKom , I 'm <disfmarker> the last SmartKom meeting I mentioned that we have some problems with the synthesis ,
speaker: B
text: which as of this morning should be resolved .
speaker: F
text: Good .
speaker: B
text: And , so ,
speaker: B
text: " should be " means they aren't yet ,
speaker: B
text: but <disfmarker> but I think I have the info now that I need .
speaker: B
text: Plus , Johno and I are meeting tomorrow ,
speaker: B
text: so maybe uh uh , when tomorrow is over , we 're done .
speaker: B
text: And ha n hav we 'll never have to look at it again
speaker: B
text: Maybe it 'll take some more time ,
speaker: B
text: to be realistic ,
speaker: B
text: but at least we 're <disfmarker> we 're seeing the end of the tunnel there .
speaker: B
text: That was that .
speaker: B
text: Um ,
speaker: B
text: the uh ,
speaker: B
text: uh I don't think we need to discuss the formalism
speaker: B
text: that 'll be done officially s once we 're done .
speaker: B
text: Um ,
speaker: B
text: something happened , in <disfmarker> on Eva 's side with the PRM that we 're gonna look at today ,
speaker: B
text: and um ,
speaker: B
text: we have a visitor from Bruchsal from the International University .
speaker: B
text: Andreas , I think you 've met everyone except Nancy .
speaker: A
text: Sorry .
speaker: C
text: Yeah .
speaker: A
text: Hi . Hi .
speaker: B
text: Hi . Hi .
speaker: B
text: And , um ,
speaker: A
text: So when you said " Andreas " I thought you were talking about Stolcke .
speaker: A
text: Now I know that we aren't ,
speaker: A
text: OK .
speaker: B
text: Andy , you actually go by Andy ,
speaker: B
text: right ?
speaker: C
text: Yeah .
speaker: B
text: Oh , OK .
speaker: C
text: Cuz there is another Andreas around ,
speaker: B
text: Eh <disfmarker>
speaker: A
text: Hmm .
speaker: C
text: so , to avoid some confusion .
speaker: B
text: That will be <pause> Reuter ?
speaker: C
text: Yeah .
speaker: B
text: Oh , OK .
speaker: B
text: So my scientific director of the EML is also the dean of the International University ,
speaker: B
text: one of his many occupations that just contributes to the fact that he is very occupied .
speaker: B
text: And , um ,
speaker: B
text: the <disfmarker>
speaker: B
text: um , he @ @ might tell us a little bit about what he 's actually doing ,
speaker: B
text: and why it is s somewhat related ,
speaker: B
text: and <disfmarker> by uh using maybe some of the same technologies that we are using .
speaker: B
text: And um . Was that enough of an update ?
speaker: F
text: I think so .
speaker: B
text: In what order shall we proceed ?
speaker: D
text: OK .
speaker: B
text: Maybe you have your on - line <disfmarker>
speaker: D
text: Uh ,
speaker: D
text: yeah , sure .
speaker: D
text: Um , so , I 've be just been looking at , um ,
speaker: D
text: Ack !
speaker: D
text: What are you doing ?
speaker: D
text: Yeah .
speaker: D
text: OK .
speaker: D
text: Um ,
speaker: D
text: I 've been looking at the PRM stuff .
speaker: D
text: Um ,
speaker: D
text: so , this is , sort of like the latest thing I have on it ,
speaker: D
text: and
speaker: D
text: I sorta constructed a couple of classes .
speaker: D
text: Like , a user class , a site class , and <disfmarker> and you know , a time , a route , and then <disfmarker> and a query class .
speaker: D
text: And I tried to simplify it down a little bit ,
speaker: D
text: so that I can actually um , look at it more .
speaker: D
text: It 's the same paper that I gave to Jerry last time .
speaker: D
text: Um ,
speaker: D
text: so basically I took out a lot of stuff , a lot of the decision nodes ,
speaker: D
text: and then tried to <disfmarker>
speaker: D
text: The red lines on the , um , graph are the um , relations between the different um , classes .
speaker: D
text: Like , a user has like , a query ,
speaker: D
text: and then , also has , you know um , reference slots to its preferences ,
speaker: D
text: um , the special needs and , you know , money , and the user interest .
speaker: D
text: And so
speaker: D
text: this is more or less similar to the flat Bayes - net that I have , you know , with the input nodes and all that .
speaker: D
text: And <disfmarker>
speaker: D
text: So I tried to construct the dependency models ,
speaker: D
text: and
speaker: D
text: a lot of these stuff I got from the flat Bayes - net ,
speaker: D
text: and what they depend on ,
speaker: D
text: and it turns out , you know , the CPT 's are really big , if I do that ,
speaker: D
text: so I tried to see how I can do , um <disfmarker> put in the computational nodes in between .
speaker: D
text: And what that would look like in a PRM .
speaker: D
text: And so I ended up making several classes <disfmarker>
speaker: D
text: Actually , you know , a class of <disfmarker> with different attributes that are the intermediate nodes ,
speaker: D
text: and one of them is like , time affordability money affordability , site availability , and the travel compatibility .
speaker: D
text: And so some of these classes are <disfmarker>
speaker: D
text: s some of these attributes only depend on stuff from , say , the user ,
speaker: D
text: or s f just from , I don't know , like the site .
speaker: D
text: S like , um , these here ,
speaker: D
text: it 's only like , user ,
speaker: D
text: but , if you look at travel compatibility for each of these factors , you need to look at a pair of , you know , what the um , preference of the user is
speaker: D
text: versus , you know , what type of an event it is ,
speaker: D
text: or you know , which form of transportation the user has
speaker: D
text: and whether , you know , the onsite parking matters to the user , in that case .
speaker: D
text: And that makes the scenario a little different in a PRM ,
speaker: D
text: because , um , then you have one - user objects
speaker: D
text: and potentially you can have many different sites in <disfmarker> in mind .
speaker: D
text: And so
speaker: D
text: for each of the site you 'll come up with this rating , of travel compatibility .
speaker: D
text: And , they all depend on the same users , but different sites ,
speaker: D
text: and that makes a <disfmarker>
speaker: D
text: I 'm tr I w I wa have been trying to see whether the PRM would make it more efficient if we do inferencing like that .
speaker: D
text: And so ,
speaker: D
text: I guess you end up having fewer number of nodes than in a flat Bayes - net ,
speaker: D
text: cuz otherwise you would <disfmarker> c
speaker: D
text: well , it 's probably the same .
speaker: D
text: But um ,
speaker: D
text: No , you would definitely have <disfmarker>
speaker: D
text: be able to re - use , like , <vocalsound> um , all the user stuff ,
speaker: D
text: and not <disfmarker> not having to recompute a lot of the stuff ,
speaker: D
text: because it 's all from the user side .
speaker: D
text: So if you changed sites , you <disfmarker> you can , you know , save some work on that .
speaker: D
text: But ,
speaker: D
text: you know , in the case where , it depends on both the user and the site , then I 'm still having a hard time trying to see how um , using the PRM will help .
speaker: D
text: Um , so anyhow , using those intermediate nodes then , this <disfmarker> this would be the class that represent the intermediate nodes .
speaker: D
text: And that would <disfmarker> basically it 's just another class in the model ,
speaker: D
text: with , you know , references to the user and the site and the time .
speaker: D
text: And then , after you group them together this <disfmarker>
speaker: D
text: no the dependencies would <disfmarker> of the queries would be reduced to this .
speaker: D
text: And so , you know , it 's easier to specify the CPT and all .
speaker: D
text: Um , so I think that 's about as far as I 've gone on the PRM stuff .
speaker: F
text: Well
speaker: D
text: Right .
speaker: F
text: No .
speaker: F
text: So y you didn't yet tell us what the output is .
speaker: D
text: The output .
speaker: F
text: So what decisions does this make ?
speaker: D
text: OK . So it only makes two decisions , in this model .
speaker: D
text: And one is basically how desirable a site is
speaker: D
text: meaning , um , how good it matches the needs of a user .
speaker: D
text: And the other is the mode of the visit ,
speaker: D
text: whether th It 's the EVA decision .
speaker: D
text: Um ,
speaker: D
text: so , instead of um , <vocalsound> doing a lot of , you know , computation about , you know , which one site it wants of <disfmarker> the user wants to visit , I 'll come <disfmarker> well , try to come up with like , sort of a list of sites .
speaker: D
text: And for each site , you know , where <disfmarker> h how <disfmarker> how well it fits ,
speaker: D
text: and basically a rating of how well it fits and what to do with it .
speaker: D
text: So .
speaker: D
text: Anything else I missed ?
speaker: F
text: So that was pretty quick .
speaker: F
text: She 's ac uh uh Eva 's got a little write - up on it
speaker: F
text: that uh , probably gives the <disfmarker> the details to anybody who needs them .
speaker: F
text: Um ,
speaker: F
text: so
speaker: F
text: the <disfmarker> You <disfmarker> you didn't look at all yet to see if there 's anybody has a implementation .
speaker: D
text: No , not yet ,
speaker: D
text: um <disfmarker>
speaker: F
text: OK .
speaker: F
text: So one <disfmarker> so one of the questions , you know , about these P R Ms is
speaker: D
text: Mm - hmm .
speaker: F
text: uh , we aren't gonna build our own interpreter ,
speaker: F
text: so if <disfmarker> if we can't find one , then we uh , go off and do something else and wait until s one appears .
speaker: F
text: Uh , so one of the things that Eva 's gonna do over the next few weeks is see if we can track that down .
speaker: F
text: Uh ,
speaker: F
text: the people at Stanford write papers as if they had one ,
speaker: F
text: but , um , we 'll see .
speaker: F
text: So w Anyway . So that 's a <disfmarker> a major open issue .
speaker: F
text: If there is an interpreter , it looks like you know , what Eva 's got should run
speaker: F
text: and we should be able to actually um , try to solve , you know , the problems ,
speaker: F
text: to actually take the data , and do it .
speaker: F
text: Uh , and we 'll see .
speaker: F
text: Uh , I actually think it is cleaner ,
speaker: F
text: and the ability to instantiate , you know , instance of people and sites and stuff , um , will help in the expression .
speaker: F
text: Whether the inference gets any faster or not I don't know .
speaker: F
text: Uh , it wouldn't surprise me if it <disfmarker> if it doesn't .
speaker: D
text: Mm - hmm .
speaker: F
text: You know , it 's the same kind of information .
speaker: F
text: I think there are things that you can express this way which you can't express in a normal belief - net ,
speaker: F
text: uh , without going to some incredible hacking
speaker: F
text: of <disfmarker> sort of rebuilding it on the fly .
speaker: F
text: I mean , the notion of instantiating your el elements from the ontology and stuff fits this very nicely and doesn't fit very well into the extended belief - net .
speaker: F
text: So that was one of the main reasons for doing it .
speaker: F
text: Um .
speaker: F
text: I don't know .
speaker: F
text: So , uh , people who have thought about the problem , like Robert
speaker: F
text: i it looked to me like if <comment> Eva were able to come up with a <vocalsound> you know , value for each of a number of uh , sites plus its EVA thing ,
speaker: F
text: that a travel planner should be able to take it from there .
speaker: F
text: And <disfmarker> you know , with some other information about how much time the person has and whatever ,
speaker: F
text: and then plan a route .
speaker: B
text: Um - hmm , um , <vocalsound> well , first of all uh , uh , great looks , mu much cleaner ,
speaker: B
text: nnn , nnn ,
speaker: B
text: Certain <disfmarker> certain beauty in it ,
speaker: B
text: so , um , if beauty is truth , then , uh we 're in good shape .
speaker: B
text: But ,
speaker: B
text: the um ,
speaker: B
text: as , uh , mentioned before we probably should look at t the details .
speaker: B
text: So if you have a write - up then uh , I 'd love to read it
speaker: D
text: Mm - hmm .
speaker: B
text: and uh <disfmarker>
speaker: B
text: because , um ,
speaker: B
text: i Can you go all the way back to the <disfmarker> the very top ?
speaker: D
text: Yeah .
speaker: B
text: Um , <vocalsound> uh these <disfmarker> @ @ <comment> these <disfmarker> w w when these are instantiated they take on the same values ? that we had before ?
speaker: D
text: I can't really see the whole thing .
speaker: B
text: or are they <disfmarker> have they changed , in a sense ?
speaker: D
text: Well I think I basically leave them to similar things .
speaker: B
text: Uh - huh .
speaker: D
text: Some of the things might <disfmarker> that might be different , maybe like <disfmarker> are that the hours for the site .
speaker: B
text: Hmm .
speaker: D
text: And , eventually I meant that to mean whether they 're open at this hour or not .
speaker: D
text: And status would be , you know , more or less like , whether they 're under construction ,
speaker: B
text: Uh - huh .
speaker: D
text: and <disfmarker> and <disfmarker> or stuff like that .
speaker: B
text: And the , uh , other question I would have is that presumably , from the way the Stanford people talk about it , you can put the probabilities also on the relations .
speaker: B
text: If <disfmarker>
speaker: D
text: Which is the structural uncertainty ?
speaker: F
text: Yeah .
speaker: F
text: Yeah , I <disfmarker> that 's <disfmarker>
speaker: F
text: That I think was actually in the previous <disfmarker> the Ubenth stuff .
speaker: F
text: I don't remember whether they carried that over to this or not ,
speaker: A
text: Mmm .
speaker: F
text: uh , structural uncertainty .
speaker: B
text: It 's sort of in the definition or <disfmarker> in the <disfmarker> in Daphne 's definition of a PRM
speaker: B
text: is that classes and relations ,
speaker: F
text: OK .
speaker: B
text: and you 're gonna have CPT 's over the classes and their relations .
speaker: F
text: Alright .
speaker: B
text: More uncertainty , or <disfmarker> or <disfmarker>
speaker: F
text: Uh ,
speaker: D
text: I remember them learning when , you know , you don't know the structure for sure ,
speaker: B
text: I should say .
speaker: F
text: Yeah .
speaker: D
text: but I don't remember reading how you specify
speaker: F
text: Right .
speaker: B
text: Yeah , that would be exactly my question .
speaker: D
text: wh to start with .
speaker: D
text: Yeah .
speaker: B
text: Well <disfmarker>
speaker: F
text: Yeah .
speaker: D
text: Yeah .
speaker: F
text: So , uh , the <disfmarker> the plan is <disfmarker> is when Daphne gets back , we 'll get in touch
speaker: F
text: and supposedly , um , we 'll actually get s deep <disfmarker> seriously connected to <disfmarker> to their work
speaker: F
text: and
speaker: B
text: Yep .
speaker: F
text: somebody 'll <disfmarker> Uh , you know <disfmarker> If it 's a group meeting once a week probably someone 'll go down and , whatever .
speaker: F
text: So , we 'll actually figure all this out .
speaker: B
text: OK .
speaker: B
text: OK .
speaker: B
text: Then I think the w <vocalsound> long term perspective is <disfmarker> is pretty clear .
speaker: B
text: We get rocking and rolling on this again , once we get a package ,
speaker: B
text: if , when , and how ,
speaker: B
text: then this becomes foregrounded
speaker: D
text: Mm - hmm .
speaker: B
text: profiled ,
speaker: B
text: focused , again .
speaker: E
text: Designated ?
speaker: A
text: Of course .
speaker: B
text: And um ,
speaker: B
text: until then we 'll come up with a something that 's <disfmarker> @ @ <comment> that 's way more complicated for you .
speaker: B
text: Right ?
speaker: D
text: OK .
speaker: B
text: Because this was laughingly easy ,
speaker: B
text: right ?
speaker: D
text: Actually I had to take out a lot of the complicated stuff ,
speaker: D
text: cuz I <disfmarker> I made it really complicated in the beginning ,
speaker: D
text: and Jerry was like , <vocalsound> " this is just too much " .
speaker: F
text: Yeah . So , um ,
speaker: F
text: you could , from this , go on and say suppose there 's a group of people traveling together
speaker: F
text: and you wanted to plan something that somehow , with some Pareto optimal uh , <vocalsound> uh , thing for <disfmarker>
speaker: A
text: That 's good .
speaker: A
text: That 's definitely a job for artificial intelligence .
speaker: F
text: uh , or <disfmarker>
speaker: B
text: Well that 's not <disfmarker> not even something humans <disfmarker>
speaker: A
text: Except for humans can't really solve it either , so .
speaker: B
text: yeah .
speaker: F
text: Right .
speaker: F
text: Right .
speaker: F
text: Well that 's the <disfmarker> that would <disfmarker> that would be a <disfmarker> uh , you could sell it , as a <disfmarker>
speaker: A
text: Yeah .
speaker: F
text: OK , eh you don't have to fight about this , just give your preferences to the <disfmarker>
speaker: A
text: And then you can blame the computer .
speaker: F
text: w Exactly .
speaker: B
text: Hmm .
speaker: A
text: So .
speaker: B
text: But what does it <disfmarker> uh <disfmarker> Would a pote potential result be to <disfmarker> to split up and never talk to each other again ?
speaker: B
text: You know .
speaker: A
text: That should be one of them .
speaker: B
text: Yeah .
speaker: F
text: Yeah . Right .
speaker: E
text: That 'd be nice .
speaker: A
text: Mmm .
speaker: F
text: Anyway .
speaker: F
text: So . So there i there are some <disfmarker> some u uh , you know , uh , elaborations of this
speaker: F
text: that you could try to put in to this structure ,
speaker: F
text: but I don't think it 's worth it now .
speaker: F
text: Because we 're gonna see what <disfmarker> what else uh <disfmarker> what else we 're gonna do .
speaker: F
text: Anyway .
speaker: F
text: But uh ,
speaker: F
text: it 's good , yeah
speaker: F
text: and <disfmarker> and there were a couple other ideas of <disfmarker> of uh , things for Eva to look at
speaker: F
text: in <disfmarker> in the interim .
speaker: B
text: Good . Then , we can move on and see what Andreas has got out his sleeve .
speaker: B
text: Or Andy , for that matter ?
speaker: C
text: OK . So uh , uh , well , thanks for having me here , first of all .
speaker: C
text: Um , so maybe just a <disfmarker> a little background on <disfmarker> on my visit .
speaker: C
text: So , uh , I 'm not really involved in any project , that 's uh <disfmarker> that 's relevant to you uh , a at the moment ,
speaker: C
text: uh , the <disfmarker> the reason is really for me uh , to have an opportunity to talk to some other researchers in the field .
speaker: C
text: And <disfmarker> and so I 'll just n sort of give you a real quick introduction to what I 'm working on ,
speaker: C
text: and um , I just hope that you have some comments
speaker: C
text: or , maybe you 're interested in it to find out more ,
speaker: C
text: and <disfmarker> and so I 'll be uh , happy to talk to you
speaker: C
text: and <disfmarker> and uh , I 'd also like to find out some more
speaker: C
text: and <disfmarker> and maybe I 'll just walk around the office
speaker: C
text: and
speaker: C
text: and then <disfmarker> and ask some <disfmarker> some questions , uh , in a couple days .
speaker: C
text: So I 'll be here for uh , tomorrow and then uh , the remainder of uh , next week .
speaker: C
text: OK , so , um , what I started looking at , uh , to begin with is just uh , content management systems uh , i i in general .
speaker: C
text: So
speaker: C
text: um , uh what 's uh <disfmarker> Sort of the state of the art there is to um <disfmarker> uh you have a bunch of <disfmarker> of uh documents or learning units or learning objects ,
speaker: C
text: um , and you store meta - data uh , associate to them .
speaker: C
text: So there 's some international standards
speaker: C
text: like the I - triple - E , uh <disfmarker> There 's an I - triple - E , LON standard ,
speaker: C
text: and um , these fields are pretty straightforward , you have uh author information , you have uh , size information , format information and so on .
speaker: C
text: Uh , but they 're two uh fields that are um , more interesting .
speaker: C
text: One is uh you store keywords associated with the uh <disfmarker> with the document ,
speaker: C
text: and one is uh , you have sort of a , um ,
speaker: C
text: well , what is the document about ? So it 's some sort of taxonomic uh , ordering of <disfmarker> of the <disfmarker> of the units .
speaker: C
text: Now , if you sort of put on your semantic glasses , uh you say , well that 's not all that easy ,
speaker: C
text: because there 's an implicit um , uh , assumption behind that
speaker: C
text: is that uh , all the users of this system share the same interpretation of the keyword and the same interpretation of uh , whichever taxonomy is used ,
speaker: C
text: and uh ,
speaker: C
text: I think that 's a <disfmarker> that 's a very <disfmarker> that 's a key point of these systems
speaker: C
text: and they sort of always brush over this real quickly without really elaborating much of that
speaker: C
text: and uh <disfmarker> As a matter of fact , the only thing that m apparently really works out so far are library ordering codes ,
speaker: C
text: which are very , very coarse grain ,
speaker: C
text: so you have some like , science , biology , and then <disfmarker>
speaker: C
text: But that 's really all that we have at the moment .
speaker: C
text: So I think there 's a huge , um , uh need for improvement there .
speaker: C
text: Now , what this uh <disfmarker> a standard like this would give us is we could um , sort of uh with a search engine just query uh , different repositories all over the world .
speaker: C
text: But we can't really <disfmarker>
speaker: C
text: Um , so what I 'm <disfmarker> what I try to do is um , to have um ,
speaker: C
text: uh <disfmarker> So . So the scenario is the following , you you 're working on some sort of project and you encounter a certain problem .
speaker: C
text: Now , what <disfmarker> what we have at our university quite a bit is that uh , students um , try to u program a certain assignment , for example ,
speaker: C
text: they always run into the same problems ,
speaker: C
text: uh , and they always come running to us ,
speaker: C
text: and they 'll say why 's it not <disfmarker> it 's not working ,
speaker: C
text: and we always give out the same answer ,
speaker: C
text: so we thought , well , it 'd be nice to have a system that could sort of take care of this ,
speaker: C
text: and so , what I want to build is basically a <disfmarker> a smart F A Q system .
speaker: C
text: Now , what you uh need to do here is you need to provide some context information
speaker: C
text: which is more elaborate than " I 'm looking for this and this and this keyword . "
speaker: C
text: So .
speaker: C
text: And I think that I don't need to tell you this .
speaker: C
text: I 'm <disfmarker> I 'm sure you have the same <disfmarker> when <disfmarker> when somebody utters a sentence in a certain , uh , context
speaker: C
text: it , and <disfmarker> and the same sentence in another context makes a huge difference .
speaker: C
text: So , I want to be able to model information like , um , so in the <disfmarker> in the context of <disfmarker> in the context of developing distributed systems , of a at a computer science school ,
speaker: C
text: um , what kind of software is the person using ,
speaker: C
text: which homework assignment is he or she working on at the moment ,
speaker: C
text: um , maybe what 's the background of that student 's
speaker: C
text: um , which um , which error message was encountered .
speaker: C
text: So this sort of information I think should be transmitted , uh , when a certain document is retrieved .
speaker: C
text: Now ,
speaker: C
text: um ,
speaker: C
text: basically giving this um <disfmarker> Uh
speaker: C
text: so we somehow need to have a formalized um , way of writing this down basically ,
speaker: C
text: and that 's where the shared interpretation of <disfmarker> of certain terms and keywords comes in again .
speaker: C
text: And , using this and some <disfmarker> some uh , knowledge about the domain I think you can do some <disfmarker> some simple inferences .
speaker: C
text: Like you know that when somebody 's working about <disfmarker> uh , working on <disfmarker> on servlets for example ,
speaker: C
text: he 's using Java , cuz servlets are used <disfmarker> are written in Java .
speaker: C
text: So some <disfmarker> some inferences like that ,
speaker: C
text: now , um , u using this you can infer more information ,
speaker: C
text: and you could then match this to the meta - data of um <disfmarker> off the documents you 're <disfmarker> you 're searching against .
speaker: C
text: So , uh what I wanna do is basically have some sort of um <disfmarker> given these inputs ,
speaker: C
text: and then I can compute how many documents match ,
speaker: C
text: and use this as a metric in the search .
speaker: C
text: Now , what I plan to do is I want to uh sort of do a uh <disfmarker> uh <pause> try to improve the quality of the search results ,
speaker: C
text: and I want to do this by having a depth uh , um , um <disfmarker> steepest descent approach .
speaker: C
text: So if I knew which operating system the person was working on , would this improve my search result ?
speaker: C
text: And <disfmarker> and having uh , uh a symbolic formalized model of this I could simply compute that ,
speaker: C
text: and find out which um <disfmarker> which questions are worth um , asking .
speaker: C
text: And that 's what I then propagate back to the user ,
speaker: C
text: and <disfmarker> and sort of try to optimize the search in this way .
speaker: C
text: Now , the big problem that I 'm facing right now is um , it 's fairly easy to hack up a system uh quickly , that <disfmarker> that works in the small domain ,
speaker: C
text: but the problem is obviously the scalability .
speaker: C
text: And uh uh , so Robert was mentioning uh , earlier today is that uh , Microsoft for example with their printer set up program has a Bayesian network ,
speaker: C
text: which does exactly this ,
speaker: C
text: but there you face a problem that these are very hard to extend .
speaker: C
text: And so , uh what I 'm <disfmarker> What I try to do is basically try to model this uh , in a way that you could really combine uh , knowledge from very different sources ,
speaker: C
text: and <disfmarker> and um , sort of looking into some of the ideas that the semantic web community uh , came up with .
speaker: C
text: Trying to <disfmarker> to have uh , an approach how to integrate s uh certain uh <disfmarker> representation of certain concepts and also some computational rules ,
speaker: C
text: um , what you can do with those .
speaker: C
text: Um .
speaker: C
text: What I 'm also looking into is a probabilistic approach into this
speaker: C
text: because document retrievals is a very fuzzy procedure ,
speaker: C
text: so it 's probably not that easy to simply have a symbolic uh , computational model .
speaker: C
text: That <disfmarker> that probably isn't expressive enough .
speaker: C
text: So . So that 's another thing ,
speaker: C
text: um , which I think you 're also uh , uh looking into right now .
speaker: C
text: And then um , uh sort of as an add - on to this whole idea , um , uh that would be
speaker: C
text: now , depending on what the search engine or the content repository <disfmarker> depending on which <disfmarker> um , uh , which uh , rules and which ontologies it <disfmarker> it uses , or basically its view of the world , uh you can get very different results .
speaker: C
text: So it might ma make a lot of sense to actually query a lot of different search engines .
speaker: C
text: And there you could have an idea where you actually have sort of a <disfmarker> a peer to peer approach ,
speaker: C
text: where we 're all sort of carrying around our individual bookshelves ,
speaker: C
text: and um , if you have a question about a homework , it 's <disfmarker> probably makes sense to ask somebody who 's in your class with you , sort of the guru in the certain area ,
speaker: C
text: rather than going to some Yahoo - like uh , search engine .
speaker: C
text: So these are some of the <disfmarker> just in a nutshell , some of the ideas .
speaker: C
text: And I think a lot of the <disfmarker> even though it 's a <disfmarker> it 's a very different domain , but I think a lot of the , um , issues are <disfmarker> are fairly similar .
speaker: C
text: So .
speaker: C
text: OK .
speaker: A
text: And so some of the <disfmarker> I don't know how much you know about the larger Heidelberg project ,
speaker: A
text: I <disfmarker> Are you <disfmarker>
speaker: C
text: Uh I know , yeah I know abou about it .
speaker: A
text: So it seems like a lot of <disfmarker> some of the issues are the same .
speaker: A
text: It 's like , um , you know , the c context - based factors that influence how you interpret ,
speaker: C
text: Mm - hmm . Mm - hmm .
speaker: A
text: um , s how to interpret .
speaker: A
text: In <disfmarker> in this case , infer in in knowing <disfmarker> wanting to know what kinds of things to ask .
speaker: A
text: We - we 've kind of talked about that ,
speaker: A
text: but we haven't worried too much about that end of the discourse .
speaker: B
text: Mm - hmm .
speaker: A
text: But maybe you guys had that in the previous models .
speaker: B
text: Well , in a <disfmarker> in one <disfmarker> t one s mmm , small difference in a <disfmarker> in a way , is that he doesn't have to come up with an answer ,
speaker: B
text: but he wants to point to the places w w
speaker: C
text: Yeah , so . So I 'm <disfmarker> I 'm not <disfmarker> I 'm not building an expert <disfmarker>
speaker: A
text: Documents that have the answers .
speaker: C
text: Uh , I want to build a smart librarian , basically
speaker: A
text: Mm - hmm .
speaker: A
text: Right .
speaker: C
text: that can point you to the right reference .
speaker: A
text: Right .
speaker: C
text: I don't wanna compute the answer ,
speaker: C
text: so it 's a little bit easier for me .
speaker: B
text: Well . Uh , you have to s still m understand what the content says about itself , and then match it to what you think the informational needs <disfmarker>
speaker: A
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: A
text: So you also don't have to figure out what the content is .
speaker: A
text: You 're just taking the keywords as a topic text ,
speaker: C
text: I <disfmarker> I assume that <disfmarker> that the there will be learning systems that <disfmarker> that tag their <disfmarker> their content .
speaker: A
text: as <disfmarker>
speaker: A
text: OK .
speaker: C
text: And um , um ,
speaker: A
text: Right .
speaker: C
text: m @ @ and basically what I <disfmarker> what I envision is that you <disfmarker> rather than just supplying a bunch of keywords you could basically <disfmarker> for <disfmarker> for an FAQ for example you could state sort of like a logic condition , when this document applies .
speaker: C
text: So " this document explains how to set up your uh , mail account on Linux " or something like this .
speaker: A
text: Mm - hmm .
speaker: C
text: So . So something <disfmarker> something very specific that you can then <disfmarker>
speaker: C
text: But the <disfmarker> I think that the key point with these uh , learning systems is that uh , a learning system is only as good as uh the amount of content it <disfmarker> it carries .
speaker: A
text: Mmm ,
speaker: A
text: mm - hmm .
speaker: C
text: You can have the best learning system with the best search interface ,
speaker: C
text: if there 's no content inside of it , it 's not very useful .
speaker: C
text: So I think ultimately because um , uh developing these <disfmarker> these rules and these inference uh <disfmarker> inferences I think is very costly ,
speaker: C
text: so um , uh I think you must be able to reuse some <disfmarker> some existing um , domain <disfmarker> domain information , or <disfmarker> or <disfmarker> or ontologies that <disfmarker> that uh other people wrote
speaker: C
text: and then try to integrate them ,
speaker: C
text: and then also search the entire web basically , rather than just the small uh , content management system .
speaker: A
text: OK .
speaker: A
text: Mm - hmm .
speaker: C
text: So I think that 's <disfmarker> that 's crucial for <disfmarker> for the success of <disfmarker> or @ @ <disfmarker>
speaker: A
text: So , you 're not <disfmarker>
speaker: A
text: I guess I 'm trying to figure out how <disfmarker> how it maps to the kinds of things that we 've talked about in this group , and , actually associated groups ,
speaker: C
text: Mm - hmm .
speaker: A
text: cuz some of us do pretty detailed linguistic analyses ,
speaker: A
text: and I 'm guessing that you <disfmarker> you won't be doing that ?
speaker: C
text: No .
speaker: A
text: OK .
speaker: A
text: Just checking .
speaker: A
text: So , <vocalsound> OK .
speaker: B
text: Hmm .
speaker: C
text: No .
speaker: A
text: So , you take the query , and <disfmarker> and <disfmarker>
speaker: F
text: On the other hand , uh , FrameNet could well be useful .
speaker: F
text: So do you know the FrameNet story ?
speaker: C
text: Um , yeah .
speaker: C
text: Uh , not <disfmarker> not too much ,
speaker: F
text: OK .
speaker: C
text: but uh ,
speaker: F
text: Oh . Th - that 's another thing you might wanna look into while you 're here .
speaker: C
text: I have a rough overview .
speaker: F
text: Because , um , you know , the standard story is that keyworks <disfmarker> keywords evoke frames , and the frames may well give you additional keywords
speaker: F
text: or uh , if you know that <disfmarker> that <disfmarker> that a <disfmarker> a bunch of keywords uh , indicate a frame , then you can find documents that actually have the whole frame , rather th than just uh , individual <disfmarker>
speaker: C
text: Mmm . Mmm .
speaker: F
text: So there 's a lot of stuff ,
speaker: F
text: and people are looking at that .
speaker: F
text: Most of the work here is just trying to get the frames right .
speaker: F
text: There 's linguists and stuff
speaker: F
text: and there 's a lot of it
speaker: F
text: and they 're <disfmarker> they 're busily working away .
speaker: F
text: But there are some application efforts trying to exploit it .
speaker: F
text: And this looks t it seems to be that this is a place where you might be able to do that .
speaker: C
text: Yeah . Yeah .
speaker: C
text: Yeah . I 'm sure I could learn a lot about um , yeah , just how to <disfmarker> how to come up with these structures ,
speaker: A
text: Mmm .
speaker: C
text: cuz it 's <disfmarker> it 's very easy to whip up something quickly ,
speaker: C
text: but it maybe then makes sense to <disfmarker> to me , but not to anybody else ,
speaker: C
text: and <disfmarker> and if we want to share and integrate things , they must <disfmarker> well , they must be well designed really .
speaker: B
text: Remember the uh , Prashant story ?
speaker: F
text: Right .
speaker: B
text: The absolutely no <disfmarker> no linguistic background person that the IU sent over here .
speaker: F
text: Right .
speaker: B
text: And Andreas and I tried to come up wi or we had come up actually with a eh <disfmarker> with him working on an interface for FrameNet , as it was back then , that would p do some of the work for this machine ,
speaker: F
text: Right .
speaker: F
text: Yeah .
speaker: B
text: which uh , never got done because Prashant found a happy occupation
speaker: F
text: W yeah , I know , I mean it <disfmarker> it <disfmarker> he <disfmarker> w he did w what <disfmarker> what he did was much more s sensible for him .
speaker: B
text: which in the <disfmarker>
speaker: F
text: I think uh ,
speaker: B
text: Absolutely . Yeah .
speaker: B
text: But so <disfmarker> I 'm just saying , the uh , we had that idea
speaker: F
text: you know <disfmarker>
speaker: F
text: Yeah .
speaker: F
text: The idea was there . Yeah , OK .
speaker: B
text: uh to <disfmarker> to exploit FrameNet there as well .
speaker: F
text: Yeah .
speaker: A
text: Hmm .
speaker: B
text: And um .
speaker: F
text: Yeah , actually you guys never <disfmarker>
speaker: B
text: And Srini 's doing information extraction also ,
speaker: B
text: right ?
speaker: F
text: Right .
speaker: B
text: with that FrameNet base .
speaker: C
text: Mmm .
speaker: F
text: Yeah .
speaker: F
text: So you <disfmarker> you guys never sent anybody else from I U .
speaker: B
text: Mm - hmm .
speaker: F
text: You were y no <disfmarker>
speaker: C
text: Except <disfmarker> except Prashant ?
speaker: F
text: Yeah .
speaker: F
text: Uh , this was supposedly an exchange program ,
speaker: C
text: Um ,
speaker: F
text: and <disfmarker> I <disfmarker> we <disfmarker> you know , it 's fine . We don't care ,
speaker: F
text: but it just <disfmarker> I 'm a little surprised that uh , Andreas didn't come up with anyone else he wanted to send .
speaker: F
text: Alright .
speaker: C
text: Uh I don't know , I mean the uh <disfmarker>
speaker: F
text: I mean I had forgotten a I <disfmarker> To be honest with you , I 'd totally forgotten we had a program .
speaker: A
text: Hmm .
speaker: B
text: Uh it 's in the program ?
speaker: C
text: Uh I <disfmarker> I think it 's <disfmarker> it 's really the lack of students uh , at IU at the moment .
speaker: F
text: Yeah .
speaker: F
text: Yeah .
speaker: F
text: No , no . There was a whole co There was a little contract signed .
speaker: F
text: It was <disfmarker>
speaker: F
text: Yeah .
speaker: C
text: Yeah , yeah . I think it 's ju it 's more the lack of <disfmarker> of students , really ,
speaker: C
text: and w we have all these sponsors that are always sort of eager to get some teams .
speaker: F
text: Yeah , I know .
speaker: A
text: Mmm .
speaker: F
text: Right .
speaker: C
text: But <disfmarker>
speaker: F
text: Right .
speaker: C
text: Well I mean if <disfmarker> if I were a student , I 'd love to come here , rather than work for some German <vocalsound> <nonvocalsound> company , or <disfmarker>
speaker: F
text: Yeah .
speaker: F
text: Right .
speaker: B
text: You are being recorded right now , so beware .
speaker: F
text: Oh , right !
speaker: C
text: Well , I didn't say anybody to <disfmarker> anything to offend <disfmarker>
speaker: C
text: well , except for the sponsors maybe ,
speaker: C
text: but <disfmarker>
speaker: F
text: Right .
speaker: F
text: Anyway .
speaker: F
text: Right . So I thi tha that 's <disfmarker> that 's one of the things that might be worth looking into while you 're here .
speaker: C
text: Mm - hmm .
speaker: F
text: Uh , unfortunately , Srini , who is heavily involved in DAML and all this sort of stuff is himself out of town .
speaker: C
text: Mm - hmm .
speaker: C
text: Well I 'll go to the uh , Semantic Web Workshop , uh , in two weeks .
speaker: F
text: Right ,
speaker: F
text: and <disfmarker>
speaker: F
text: Yeah , for <disfmarker> for some reason he 's not doing that .
speaker: A
text: Yeah .
speaker: F
text: I don't know why he @ @ <disfmarker>
speaker: A
text: Well , he had other things to do .
speaker: F
text: oh , I , who knows ?
speaker: F
text: Anyway , s yeah , you 'll see <disfmarker> you 'll certainly see a lot of the people there .
speaker: A
text: The uh <disfmarker>
speaker: A
text: The other person I thought of is Dan Gildea ?
speaker: A
text: because he did some work on topic spotting
speaker: F
text: Yeah .
speaker: F
text: St - statistical stuff .
speaker: F
text: That would be a very good idea .
speaker: A
text: w um , which is , I mean , you <disfmarker> I mean . I don't <disfmarker> Depending on how well you wanna integrate with that end ,
speaker: C
text: Mm - hmm .
speaker: A
text: you know , like , taking the data and fig you said the learning systems that figure out <disfmarker>
speaker: A
text: We <disfmarker> There 's someone in ICSI who actually has been working on <disfmarker> has worked on that kinda stuff ,
speaker: A
text: and he 's worked with frame net ,
speaker: A
text: so you could talk to him about , you know , both of those things at once .
speaker: C
text: Mm - hmm . Mm - hmm .
speaker: A
text: So .
speaker: A
text: And he just finished writing a draft of his thesis .
speaker: A
text: So .
speaker: C
text: So , uh , who is that again ?
speaker: A
text: I u <vocalsound> Dan Gildea , GILDEA .
speaker: A
text: And , he 's in one of the rooms on the fifth floor and stuff ,
speaker: B
text: Who ?
speaker: B
text: I can take you to his office .
speaker: B
text: It 's just around the corner .
speaker: A
text: and <disfmarker>
speaker: C
text: OK , great .
speaker: A
text: Hmm .
speaker: A
text: Well , if you fal solve the problem , <vocalsound> hope you can do one for us too .
speaker: F
text: Alright , was there anything else for this ?
speaker: F
text: One of these times soon we 're gonna hear about construal .
speaker: B
text: Yeah . I 'm sure .
speaker: B
text: I have um <disfmarker> I think it was November two thousand three or some <disfmarker>
speaker: B
text: No . Wh - I had something in my calendar .
speaker: F
text: Oh , OK . Right .
speaker: B
text: Um ,
speaker: E
text: Wait a second .
speaker: E
text: That 's a long way away .
speaker: F
text: Good thinking !
speaker: B
text: Uh well , maybe I can <disfmarker> I can bribe my way out of this .
speaker: B
text: So .
speaker: B
text: So I did some double checking
speaker: B
text: and it seems like spring break in two thousand <vocalsound> one .
speaker: A
text: Talk about changing the topic .
speaker: B
text: No .
speaker: F
text: Well , no , but he 's <disfmarker> he 's <disfmarker> he 's <disfmarker> he 's <disfmarker> as you said , he 's , like the state legislature , he 's trying to offer us bribes .
speaker: A
text: At least this is a private meeting .
speaker: A
text: Right , exactly ,
speaker: A
text: OK , that 's the link .
speaker: B
text: This uh <disfmarker> Oh , they refused the budget again ?
speaker: B
text: Is it <disfmarker> so about CITRIS ?
speaker: B
text: Yeah , still nothing .
speaker: F
text: Uh , this <disfmarker> this <disfmarker> this <disfmarker> t the s
speaker: F
text: we 're , uh , involved in a literally three hundred million dollar uh , program .
speaker: F
text: Uh , with the State of California .
speaker: F
text: And , the State of California is now a month and a half behind its legis its legally required date to approve a budget .
speaker: F
text: So the budget has not been approved .
speaker: F
text: And two days ago <disfmarker> There 's two l you know , so , two branches of legislature . One branch approved it ,
speaker: C
text: Mm - hmm .
speaker: F
text: and , um ,
speaker: F
text: yesterdayday <comment> there was this uh <disfmarker> uh I thought that the other branch would just approve it ,
speaker: F
text: but now there 's actually a little back sliding to people who <disfmarker> who approved it got flak from there ,
speaker: F
text: eh anyway .
speaker: F
text: So , um <disfmarker>
speaker: F
text: Oh ! I have to tell you a wonderful story about this ,
speaker: F
text: OK ?
speaker: F
text: And then we 'll go .
speaker: F
text: So , I <disfmarker> it turns out I wound up having lunch today with a guy named Tom Kalil .
speaker: F
text: KILL <disfmarker>
speaker: F
text: KALIL .
speaker: F
text: And , uh , he now works at Berkeley .
speaker: F
text: In fact he 's hired to run a lot of CITRIS ,
speaker: F
text: even though we don't have the money they <disfmarker> So they 've been hiring people right and left ,
speaker: F
text: so , uh , they think the money 's coming .
speaker: F
text: So <disfmarker> and he was , I think , the chief staffer to Clinton on technology matters .
speaker: F
text: He was in the White House ,
speaker: F
text: I don't remember what he was saying .
speaker: F
text: A anyway , like that .
speaker: F
text: And , is now doing all the politics for CITRIS ,
speaker: F
text: but also , has a uh , a lot of interest in uh , actually doing things for society ,
speaker: F
text: so digital divide and stuff like that .
speaker: F
text: So that 's s interesting to me but maybe not to you .
speaker: F
text: But the really interesting thing was , he st he s he s said something about , you know I 'm interested in things that have high social multiplier ,
speaker: F
text: something that is of great social value .
speaker: F
text: He said , " for example " ,
speaker: F
text: this was his only example ,
speaker: F
text: " if you had a adult literacy program that was as good as an individual tutor ,
speaker: F
text: and as compelling as a video game ,
speaker: F
text: then that would have a huge social impact " .
speaker: F
text: I said , " Oh great ! That 's a good problem to work on . "
speaker: F
text: Anyway . So it was nice that uh , he 's got this view , of A , that 's what you should try to do , and B , uh , language would be a good way to do it .
speaker: F
text: So that 's <disfmarker>
speaker: A
text: Mmm . Definitely .
speaker: F
text: So anyway , that 's the end of the story .
speaker: A
text: But for adults and not for the children .
speaker: F
text: This was <disfmarker>
speaker: F
text: Yeah . I didn't push him on the ch on the child thing ,
speaker: A
text: Uh - huh .
speaker: F
text: but , uh ,
speaker: F
text: you know , a again , if <disfmarker> if you <disfmarker> if you
speaker: A
text: Oh .
speaker: F
text: um ,
speaker: F
text: and this was <disfmarker> this was literacy ,
speaker: F
text: which actually is somewhat different problem .
speaker: A
text: Mm - hmm .
speaker: F
text: Maybe easier .
speaker: F
text: I don't know .
speaker: F
text: So this is reading , rather than teaching <disfmarker>
speaker: F
text: Another project we started on , and <disfmarker> and didn't get funded for was , uh , to try to build an automatic tutoring program , for kids whose first language wasn't English .
speaker: F
text: Which is like half the school population in California .
speaker: F
text: Something like that ,
speaker: F
text: isn't it ?
speaker: A
text: Mm - hmm .
speaker: F
text: Yeah .
speaker: F
text: So ,
speaker: F
text: enormous problem in California ,
speaker: F
text: and the idea was if we 're so smart about language understanding and speech understanding , couldn't we build <vocalsound> uh , programs that would be tutors for the kids .
speaker: F
text: We think we could .
speaker: F
text: Anyway . So <disfmarker> so <disfmarker> But this is a slightly different problem ,
speaker: A
text: Mm - hmm .
speaker: F
text: and
speaker: F
text: um ,
speaker: F
text: I know none of us have the spare time to look at it right now ,
speaker: F
text: but it i it 's <disfmarker> it 's interesting
speaker: F
text: and I may um , talk to him some more about is em somebody already doing this , and stuff like that .
speaker: F
text: So anyway , that was <disfmarker> that was today 's little story .
speaker: E
text: Hmm .
speaker: B
text: OK . So I <disfmarker> I did manage to get <disfmarker> pull my head out of the sling by sidetracking into CITRIS ,
speaker: F
text: No , no .
speaker: B
text: but uh or <disfmarker> a temporarily putting it out of the sling
speaker: F
text: Right .
speaker: B
text: but , I <disfmarker> I 'll volunteer to put it right back in by stating that I am n uh among some other things in the process of writing up stuff that we have been discussing at our daily meetings ,
speaker: F
text: Yeah .
speaker: B
text: and also revising ,
speaker: B
text: thanks for all the comments ,
speaker: B
text: the c the original construal proposal .
speaker: B
text: And , if I put one and one together , I may end up with a number that 's greater than one and that I <disfmarker> I can potentially present once you get back .
speaker: A
text: Greater than two ?
speaker: F
text: You 're good .
speaker: B
text: Nnn . <comment> s sometimes , you know the sum is not uh less than the <disfmarker>
speaker: A
text: Uh , right , right .
speaker: F
text: Right .
speaker: F
text: Right .
speaker: F
text: Anyway . Yeah , so <disfmarker> OK , so that 'd be great ,
speaker: F
text: but I 'd <disfmarker> I think it 's <disfmarker> it 's time again ,
speaker: F
text: right ?
speaker: B
text: Absolutely . Yeah .
speaker: F
text: Yeah . OK .
speaker: B
text: But um , and hopefully all sidetracking um , other things will have disappeared , soon .
speaker: F
text: Good .
speaker: F
text: Yep .
speaker: F
text: Done ?

Meeting summary:
text: The first phase of the data collection has finished.
text: There is a new wizard for phase two, during which subjects will be given more complex scenarios.
text: Also finished are the modifications on SmartKom: the remaining glitches will take no more than a day to iron out.
text: A big part of the meeting was covered by the presentation of the PRM of the proposed system.
text: An alternative representation of the Bayes-net, it depicts context features as classes, and dependencies as relations between them.
text: The current outputs show the desirability of a site, as well as its EVA mode.
text: The fact that this model allows for instantiations of classes fits the research purposes much better than the extended belief-net.
text: Following this, a visiting researcher presented an overview of a parallel project at the International University.
text: It attempts to build a smart tutoring system for a computer science course.
text: The assumption is that document searches can give more personalised results, if they take into account contextual parameters (user, situation).
text: Although no detailed linguistic analysis takes place, it was suggested that the use of FrameNet could be a useful approach.
text: There were also further suggestions for meetings with ICSI researchers.
text: As the data collection is going into its second phase, more complex scenarios will be used to generate more intricate dialogues.
text: Subjects can be recruited from within the Psychology department students, since such participation in experiments is compulsory in their syllabus.
text: As the work on creating a PRM for the system has progressed, it is now necessary to find an implementation that can work as a PRM interpreter.
text: There are no plans to build one from scratch, but it seems that other research groups at Stanford may already have one.
text: Moreover, closer collaboration with the local PRM group will be pursued with additional participation in their meetings.
text: An early version of the PRM of the system presented the same problem as a flat Bayes-net: the CPT's become too large.
text: Another PRM issue that arose and remained unclear was how the probabilities are specified (instead of learnt) on the actual relations between the classes.
text: Furthermore, the lack of a PRM interpreter is an open issue.
text: It is not within the remit of the project to build one from scratch, therefore, an existing implementation has to be found.
text: On the other hand, the discussion about the smart tutoring system being built at the International University showed the importance of finding out  which context parameters are influential in a given domain.
text: It is easy to hack up a system for a small domain, but making it scalable is much more difficult.
text: Finally, there was a passing mention of problems encountered with the speech synthesis module of SmartKom.
text: The first phase of the data collection has been completed.
text: Thirty subjects were recorded in total.
text: Of those dialogues, ten have been transcribed.
text: A new wizard will carry out the second phase.
text: On top of this, a presentation of a PRM of the proposed system took place.
text: The PRM comprises a set of classes, such as "user", "site", "route", "time" and "query" with relations between them.
text: Another class incorporates a number of attributes ("money affordability", "travel compatibility" etc) modelling the intermediate nodes of the Bayes-net, as well as references to the other classes in the model.
text: This model is much cleaner, and it also makes it easier to specify the CPT's.
text: At this stage, the model makes two decisions (outputs): how much a site fits a user's needs and what the user intention is in EVA terms.