PhD A: Tubingen was at least involved in putting the chunks together
Grad D: In Tub - at PhD A: I can't quite recall whether they actually produced the chunks in the first place .
Grad D: oh
Professor F: Uh . I see . Yeah , that 's right .
PhD A: Or wh
Grad D: Oh from from Stuttgart ,
Professor F: There w That 's right . They w They had There were This was done with a two phase thing , where {comment} the chunk parser itself was pretty stupid
Grad D: yeah , also
Professor F: and then there was a kind of trying to fit them together that h used more context .
PhD A: Right . Yeah
Professor F: Right ?
PhD A: Well you s and and especially you did some some um , l um was a learning - based approach which learned from a big corpus of of trees .
Grad D: Mm - hmm .
Professor F: Right .
PhD A: And yes the it the chunk parser was a finite - state machine that um Mark Light originally w worked on in while he was in Tuebingen
Professor F: Right .
PhD A: and then somebody else in Tuebingen picked that up . So it was done in Tuebingen , yeah . Definitely .
Professor F: But is that the kind of thing y It sounds like the kind of thing that you were thinking of .
PhD A: Yeah I guess it 's similar .
Grad D: yeah . yeah that 's In this direction , yes
Professor F: What ?
Grad D: Yeah , it 's in in this direction .
Grad B: The Professor F: Hmm .
Grad B: From Michael Strube , I 've heard very good stuff about the chunk parser that is done by FORWISS , uh , which is in embassy doing the parsing .
PhD A: Mm - hmm .
Grad B: So this is sort of came as a surprise to me that you know , embassy s {comment} is featuring a nice parser but it 's what I hear . One could also look at that and see whether there is some synergy possible .
Grad D: Mm - hmm , yeah , it would be very interesting , Mm - hmm . Mmm , yeah .
Grad B: And they 're doing chunk parsing and it 's uh I I can give you the names of the people who do it there . But um . Then there is of course more ways of parsing things .
Professor F: Of course . But But uh given th the constraints , that you want it to be small and fast and so forth , my guess is you 're probably into some kind of chunk parsing . And uh I 'm not a big believer in this um statistical you know , cleaning up uh It That seems to me kind of a last resort if uh you can't do it any other way . uh but I dunno .
Grad D: Hmm .
Professor F: It may i i may be that 's what you guys finally decide do . Uh . And have you looked uh just again for context Grad D: Mm - hmm .
Professor F: There is this this one that they did at SRI some years ago Fastus ?
Grad D: um
Professor F: a Grad D: yeah , I 've I 've looked at it but but it 's no not much uh information available . I found ,
Professor F: ah !
Grad D: but it 's also finite - state transducers , I thought .
Professor F: It is . Yeah . I mean it 's it was pretty ambitious .
Grad D: and
Professor F: And of course it was English oriented ,
Grad D: Yeah , and and Purely finite - state transducers are not so good for German since there 's um
Professor F: um w Right .
Grad D: The word order is is uh not fixed
Professor F: Yeah , I guess that 's the point is is all the morphology and stuff . And English is all th all word order . And it makes a lot more sense .
Grad D: Yeah .
Professor F: And e Yeah , OK . Good point . So in in in German you 've got uh most of this done with
Grad D: Mm - hmm . Also it 's uh it 's um Yes , uh the um choice between uh this processing and that processing and my template matcher .
Professor F: Right . Right .
Grad D :
Professor F: So what about Um Did y like Morfix ? a a e y you 've got stemmers ? Or is that something that Grad D: Um , yeah but it 's all in the in the lexicon . So it 's Professor F: But did you have that ?
Grad D: Yeah th the information is available .
Professor F: OK . I see . So , but Grad D: So Professor F: So y you just connect to the lexicon
Grad D: Yeah
Professor F: and uh at least for German you have all all of the uh the stemming information .
Grad D: Yeah , we can , oh yeah . We have knowledge bases from from Verbmobil system we can use
Professor F: Yep .
Grad D: and so .
Professor F: Right . But it it it doesn't look like i you 're using it . I didn't n see it being used in the current template uh parser . I I didn't see any Uh of course we l actually only looked at the English .
Grad D: It um
Professor F: Did we look at the German ? I don't remember .
Grad D: Yeah , but but it 's used for for stem forms .
Professor F: So w wha
PhD A: n Well I think I think there 's some misunderstanding here
Professor F: i
PhD A: it 's Morphix is not used on - line .
Grad D: Oh , OK .
PhD A: s so the lexicon might be derived by Morphix
Grad D: What ?
PhD A: but What what 's happening on - line is just um um a a retrieval from the lexicon which would give all the stemming information
Professor F: Right .
Grad D: Hmm .
Professor F: Right .
PhD A: so it would be a full foreign lexicon .
Professor F: And that 's what you have .
PhD A: Yep .
Grad D: Yeah
Professor F: OK .
Grad B: We threw out all the forms .
Professor F: What uh I didn't reme
Grad B: We threw out all the forms
Professor F: Huh ?
Grad B: because , you know , English , well Professor F: Oh OK , so it yeah , s s I thought I 'd Grad D: Mm - hmm .
Professor F: So in German then you actually do case matching and things like in the in the pattern matcher or not ?
Grad D: um Not yet but it 's planned to do that .
Professor F: OK . Cuz I r I didn't reme I didn't think I saw it .
Grad D: Yeah
Professor F: Have we looked at the German ? Oh , I haven yeah that 's getting it from the lexicon is just fine .
PhD A: Sure , right .
Grad D: Oh yes .
Professor F: Yeah , yeah , yeah . No problem with that . um Yeah and here 's the case where the English and the German might really be significantly different . In terms of if you 're trying to build some fast parser and so forth and You really might wanna do it in a significantly different way . I don't know . So you 've you guys have looked at this ? also ? in terms of You know , w if you 're doing this for English as well as German Um Do you think now that it would be this doing it similarly ?
Grad D: um Yeah , it 's um I think it 's um yes , it 's it 's um possible to to do list processing . and Maybe this is um more adequate for English and in German um set processing is used .
Professor F: Set .
Grad D: Maybe yeah . Some extensions uh have to be made . For for a English version
Professor F: Mmm . OK . Interesting . Not easy .
Grad B: Well there 's m I 'm sure there 's gonna be more discussion on that after your talk .
Grad D: Mm - hmm ,
Grad B: We 're just gonna foreshadow what we saw that
Grad D: yeah .
Professor F: Right . Right .
Grad B: and um
Professor F: Now actually , um Are you guys free at five ? Or Do you have to go somewhere at five o ' clock tonight ? W in ten minutes ?
Grad D: Ah PhD A: uh uh I think we 're expect Grad D: mmm . No . Oder there was an talk ?
PhD A: Yeah , there there 's the um practice talk .
Grad D: uh Mmm , yeah .
Professor F: Great . So you 're going to that .
PhD A: Yeah , that that 's what we were planning to do .
Professor F: That 's good , because that will uh tell you a fair amount about The form of semantic construction grammar that we 're using .
PhD A: Yeah . Mm - hmm .
Professor F: so So I th I think that probably as good an introduction as you 'll get .
Grad D: Ah .
Professor F: Uh to the form of of uh conceptual grammar that that w we have in mind for this .
Grad D: Mmm , ah .
Professor F: It won't talk particularly about how that relates to what uh Robert was saying at the beginning . But let me give you a very short version of this . So we talked about the fact that There 're going to be a certain number of decisions That you want the knowledge modeler to make , that will be then fed to the function module , that does uh , route planning . It 's called the " route planner " or something .
PhD A: Mm - hmm .
Professor F: So there are these decisions . And then one half of this we talked about at little bit is how if you had the right information , if you knew something about what was said and about th the something about was the agent a tourist or a native or a business person or uh young or old , whatever . That information , and also about the Uh , what we 're calling " the entity " , Is it a castle , is it a bank ? Is it a s town square , is it a statue ? Whatever . So all that kind of information could be combined into decision networks and give you decisions . But the other half of the problem is How would you get that kind of information from the parsed input ? So , um So what you might try to do is just build more templates , saying uh we 're trying to build a templ you know build a template that w uh somehow would capture the fact that he wants to take a picture .
Grad D: Mmm .
Professor F: OK ? And and we could you could do this . And it 's a small enough domain that probably you , you know Grad D: Mmm .
Professor F: OK . You could do this . But uh from our point of view this is also a research project and there are a couple of people not here for various reasons who are doing doctoral dissertations on this ,
PhD A: Mm - hmm .
Professor F: and the idea that we 're really after is a very deep semantics based on cognitive linguistics and the notion that there are a relatively small number of primitive conceptual schemas that characterize a lot of activity . So a typical one in this formulation is a container . So this is a static thing . And the notion is that all sorts of physical situations are characterized in terms of containers . Going in and out the portals and con
Grad D: Mmm .
Professor F: OK . But also , importantly for Lakoff and these guys is all sorts of metaphorical things are also characterized this way . You get in trouble and you know et cetera
Grad D: Mmm .
Professor F: and so s So , what we 're really trying to do is to map from the discourse to the conceptual semantics level . And from there to the appropriate decisions .
PhD A: Mm - hmm .
Professor F: So another one of these primitive , what are called " image schemas " , is uh goal seeking . So this a notion of a source , path , goal , trajector , possibly obstacles .
PhD A: Mm - hmm .
Professor F: And the idea is this is another conceptual primitive .
PhD A: Mm - hmm .
Professor F: And that all sorts of things , particularly in the tourist domain , can be represented in terms of uh source , path and goal . So the idea would be could we build an analyser that would take an utterance and say " Aha ! th this utterance is talking about an attempt to reach a goal . The goal is this , the pers the , uh traveller is that , uh the sor w where we are at now is is this , they 've mentioned possible obstacles , et cetera . " So th the and this is an again attempt to get very wide coverage . So if you can do this , then the notion would be that across a very large range of domains , you could use this deep conceptual basis as the interface .
PhD A: Mm - hmm . Mm - hmm .
Professor F: And then , uh The processing of that , both on the input end , recognizing that certain words in a language talk about containers or goals , et cetera , and on the output end , given this kind of information , you can then uh make decisions about what actions to take . Provides , they claim , a very powerful , general notion of deep semantics . So that 's what we 're really doing .
PhD A: Mm - hmm .
Professor F: And Nancy is going to Her talk is going to be not about using this in applications , but about modeling how children might learn this kind of uh deep semantic grammar .
PhD A: Mm - hmm . Yep , yep . And how do you envision um the the um this deep semantic to be worked with . Would it be highly ambiguous if and then there would be another module that takes that um highly underspecified deep semantic construction and map it onto the current context to find out what the person really was talking about in that context . or or a Professor F: Well that 's that 's that 's where the belief - net comes in . So th the idea is , let 's take this business about going to the Powder - Tower .
PhD A: Mm - hmm .
Professor F: So part of what you 'll get out of this will be the fact tha w if it works right , OK , that this is an agent that wants to go to this place and that 's their goal
PhD A: Mm - hmm .
Professor F: and there will be additional situational information .
PhD A: Oh , OK .
Professor F: Uh , OK ,
PhD A: th
Professor F: part of it comes from the ontology . The tower is this kind of object .
PhD A: Mm - hmm . Yeah , OK .
Professor F: Part of it comes from the user model .
PhD A: Mm - hmm .
Professor F: And the idea of the belief - net is it combines the information from the dialogue which comes across in this general way ,
PhD A: Mm - hmm .
Professor F: you know this is a this is a goal seeking behavior , along with specific information from the ontology about the kinds of objects involved
PhD A: Yeah OK , Yeah , yep yep yep yep
Professor F: and about the situation about " Is it raining ? " I don't know . Whatever it is . And so that 's the belief - net that we 've laid out .
PhD A: Mm - hmm .
Professor F: And so th the coupling to the situation comes in this model from , at th at th at the belief - net , combining evidence from the dialogue with the ontology with the situation .
PhD A: Yeah .
Grad D: Hmm .
Professor F: But Nancy isn't gonna talk about that ,
PhD A: Yeah , oh yeah , I see ,
Professor F: just about the um
PhD A: yeah yeah , really .
Grad B: First steps .
Professor F: Right . The the construction grammar .
Grad B: And she 's gonna start in a minute .
Professor F: In a minute .
Grad D: Ah , OK .
Professor F: OK .
PhD G: Is it i in , then , your place , in five five - A ?
PhD A: Alright .