F: I I I d I 'm asking .
F: I don't know .
D: Yeah , uh the problem is th that it has to be very fast
D: because um if you want to for more than one path anywhere
F: OK .
D: what 's in the latches from the speech recognizer
F: Mm - hmm .
D: so it 's speed is crucial .
D: uh
D: And they are not fast enough .
F: Mm - hmm .
D: And they also have to be very robust . cuz of um speech recognition errors
D: and
F: OK .
F: So , um So there was a chunk parser in Verbmobil , that was one of the uh branchers .
F: You know they d th I c
F: There were these various uh , competing uh syntax modules .
F: And I know one of them was a chunk parser
F: and I don't remember who did that .
D: I think it 's that
B: A Alan ?
D: might ,
D: at Tuebingen I thought .
F: Yeah I d I don't remember .
D: was Do you know something about that ?
A: Tubingen was at least involved in putting the chunks together
D: In Tub - at A: I can't quite recall whether they actually produced the chunks in the first place .
D: oh
F: Uh . I see .
F: Yeah , that 's right .
A: Or wh
F: There w
D: Oh from from Stuttgart ,
F: That 's right .
F: They w They had There were This was done with a two phase thing , where <comment> the chunk parser itself was pretty stupid
D: yeah , also
F: and then there was a kind of trying to fit them together that h used more context .
A: Right .
A: Yeah
F: Right ?
A: Well you s and and especially you did some some
A: um , l um
A: was a learning - based approach which learned from a big corpus of of trees .
F: Right .
D: Mm - hmm .
F: Right .
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
A: and then somebody else in Tuebingen picked that up . So it was done in Tuebingen ,
A: yeah .
A: Definitely .
F: But is that the kind of thing y
F: It sounds like the kind of thing that you were thinking of .
D: yeah .
A: Yeah I guess it 's similar .
D: yeah
D: that 's In this direction ,
D: yes
F: What ?
D: Yeah , it 's in in this direction .
F: Hmm .
B: The B: From Michael Strube , I 've heard very good stuff about the chunk parser that is done by FORWISS ,
B: uh , which is in embassy doing the parsing .
A: Mm - hmm .
B: So this is sort of came as a surprise to me that you know , embassy s <comment> is featuring a nice parser
B: but it 's what I hear . One could also look at that and see whether there is some synergy possible .
D: Mm - hmm , yeah ,
D: it would be very interesting ,
D: Mm - hmm .
D: Mmm , yeah .
B: And they 're doing chunk parsing
B: and it 's uh B: I I can give you the names of the people who do it there .
B: But um .
B: Then there is of course more ways of parsing things .
F: Of course .
F: 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 .
F: And uh I 'm not a big believer in this um statistical you know , cleaning up
F: uh
F: It That seems to me kind of a last resort if uh you can't do it any other way .
F: uh
F: but I dunno .
F: It may i i may be that 's what you guys finally decide do .
D: Hmm .
F: Uh .
F: And have you looked F: uh just again for context D: Mm - hmm .
F: There is this this one that they did at SRI some years ago F: Fastus ?
D: um
F: a D: yeah , I 've I 've looked at it
D: but but it 's no not much uh information available .
F: ah !
D: I found ,
D: but it 's also finite - state transducers , I thought .
F: It is .
F: Yeah .
F: I mean it 's it was pretty ambitious .
D: and
F: And of course it was English oriented ,
F: um w
D: Yeah , and and Purely finite - state transducers are not so good for German since there 's um
F: Right .
F: Yeah , I guess that 's the point is is all the morphology and stuff .
D: The word order is is uh not fixed
F: And English is all th all word order .
F: And it makes a lot more sense .
D: Yeah .
F: And e
F: Yeah , OK .
F: Good point .
F: So in in in German you 've got uh most of this done with
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 .
F: Right .
F: Right .
F: So what about
F: Um
F: Did y
F: like Morfix ?
F: a a e y you 've got stemmers ?
F: Or is that something that D: Um , yeah but it 's all in the in the lexicon .
F: But did you have that ?
D: So it 's D: Yeah th the information is available .
F: OK .
F: I see .
F: So , but F: So y you just connect to the lexicon
D: So D: Yeah
F: and uh
F: at least for German you have all all of the uh the stemming information .
D: Yeah , we can ,
D: oh yeah .
D: We have knowledge bases from from Verbmobil system we can use
F: Yep .
D: and so .
F: Right .
F: But it it it doesn't look like i you 're using it .
F: I didn't n see it being used in the current template uh parser .
F: I I didn't see any Uh F: of course we l actually only looked at the English .
D: It F: Did we look at the German ?
D: um
F: I don't remember .
F: So w wha
D: Yeah , but but it 's used for for stem forms .
A: n Well I think I think there 's some misunderstanding here
F: i
D: Oh , OK .
A: it 's Morphix is not used on - line .
A: s so the lexicon might be derived by Morphix
D: What ?
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
F: Right .
F: Right .
D: Hmm .
A: so it would be a full foreign lexicon .
F: And that 's what you have .
D: Yeah
A: Yep .
F: OK .
F: What F: uh I didn't reme
B: We threw out all the forms .
F: Huh ?
B: We threw out all the forms
B: because , you know , English ,
B: well F: Oh OK ,
F: so it yeah , s s I thought I 'd D: Mm - hmm .
F: So in German then you actually do case matching and things like in the in the pattern matcher or not ?
D: um Not yet but it 's planned to do that .
F: OK .
F: Cuz I r I didn't reme I didn't think I saw it .
F: Have we looked at the German ?
D: Yeah
F: Oh , I haven
F: yeah
F: that 's getting it from the lexicon is just fine .
F: Yeah , yeah , yeah .
A: Sure ,
D: Oh yes .
A: right .
F: No problem with that .
F: um
F: Yeah and here 's the case where the English and the German might really be significantly different .
F: In terms of if you 're trying to build some fast parser and so forth
F: and F: You really might wanna do it in a significantly different way .
F: I don't know .
F: So you 've you guys have looked at this ?
F: also ?
F: in terms of You know , w if you 're doing this for English as well as German
F: Um Do you think now that it would be this doing it similarly ?
D: um Yeah ,
D: it 's um I think it 's um
D: yes , it 's it 's um possible to to do list processing .
D: and Maybe this is um more adequate for English
D: and in German um set processing is used .
F: Set .
D: Maybe yeah .
D: Some extensions uh have to be made . For for a English version
F: Mmm .
F: OK .
F: Interesting .
F: Not easy .
B: Well there 's m I 'm sure there 's gonna be more discussion on that after your talk .
D: Mm - hmm ,
F: Right .
D: yeah .
B: We 're just gonna foreshadow what we saw that
F: Right .
B: and um
F: Now actually ,
F: um
F: Are you guys free at five ?
F: Or Do you have to go somewhere at five o ' clock tonight ?
F: W in ten minutes ?
D: Ah D: mmm . No .
A: uh uh I think we 're expect D: Oder there was an talk ?
A: Yeah , there there 's the um practice talk .
D: uh Mmm , yeah .
F: Great .
F: So you 're going to that .
A: Yeah , that that 's what we were planning to do .
F: That 's good , because that will uh tell you a fair amount about The form of semantic construction grammar that we 're using .
A: Yeah .
F: so So I th I think that probably as good an introduction as you 'll get .
A: Mm - hmm .
D: Ah .
F: Uh to the form of of uh conceptual grammar that that w we have in mind for this .
D: Mmm , ah .
F: It won't talk particularly about how that relates to what uh Robert was saying at the beginning .
F: But let me give you a very short version of this .
F: 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 .
F: It 's called the " route planner " or something .
A: Mm - hmm .
F: So there are these decisions .
F: And then one half of this we talked about at little bit is how if you had the right information ,
F: if you knew something about what was said
F: and about th the something about was the agent a tourist or a native or a business person
F: or uh young or old ,
F: whatever .
F: That information ,
F: and also about the
F: Uh , what we 're calling " the entity " ,
F: Is it a castle ,
F: is it a bank ?
F: Is it a s town square ,
F: is it a statue ?
F: Whatever .
F: So all that kind of information could be combined into decision networks and give you decisions .