Steve Beale. 1997. Hunter-Gatherer: applying constraint satisfaction, branch-and-bound and solution synthesis to computational semantics. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....mechanism that does not impose a strict order on making decisions in linearizing the discourse tree. Various ways of implementing such a scheme can be imagined; one is the blackboard based approach suggested by Wanner and Hovy [1996] another is the Hunter Gatherer search paradigm introduced by Beale [1996]. 6 Summary present day text generation systems typically employ quite simplified approaches for signalling discourse relations in text. Our work aims at enabling generators to truly choose discourse markers on the basis of generation parameters and context. This way, we gain variety in marker ....

Beale, S. 1996. Hunter-Gatherer: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Computational Semantics. NMSU Technical Report, MCCS-96-289.

....mechanism that does not impose a strict order on making decisions in linearizing the discourse tree. Various ways of implementing such a scheme can be imagined; one is the blackboard based approach suggested by Wanner and Hovy [1996] another is the Hunter Gatherer search paradigm introduced by Beale [1996]. 6 Summary Present day text generation systems typically employ quite simplified approaches for signalling discourse relations in text. Our work aims at enabling generators to truly choose discourse markers on the basis of generation parameters and context. This way, we gain variety in marker ....

Beale, S. 1996. Hunter-Gatherer: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Computational Semantics. NMSU Technical Report, MCCS-96-289.

....inter connectedness of the problem. A problem with 80 vertices, with the average vertex connected by 6 edges sets up a relatively local problem space. It may be possible to break such a problem up into manageable sub problems. Natural language processing, which generally has tree shaped graphs (Beale, et al., 1996), has a low average inter connectivity. The N Queens problem, where every decision affects every other decision, is an example of a clique with high inter connectivity. The traveling salesperson problem can probably be reduced to one with relatively EXPLOITING GRAPH TOPOLOGY. 3 small ....

....a computational strategy which attacks problems of this kind. We integrate three related AI search techniques constraint satisfaction, branchand bound and solution synthesis and direct each at minimally interacting subproblems. The methodology called HUNTER GATHERER (HG) was introduced in (Beale, et al., 1996) and can be summarized as follows: ffl branch and bound and constraint satisfaction allow us to hunt down nonoptimal and impossible solutions and prune them from the search space. ffl novel solution synthesis methods then gather all optimal solutions. Below, we briefly review some of the ....

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Beale, Stephen. 1996. Hunter-Gatherer: applying constraint satisfaction, branch-and-bound and solution synthesis to natural language semantics. Tech. Report, MCCS-96-289, Computing Research Lab, New Mexico State Univ.

....Efficient Constraint based Processin g The Mikrokosmo s project utilizes an efficient, constraintsdirected control architecture called Hunter Gatherer (HG) Beale et al..1996] overviews how it enables semantic analysis to be performed in near linear time. Its use in generation is quite similar. Beale1997] describes HG in detail. Consider Figure 8, a representation of the constraint interactions present in a section of Figure 1. Each label, such as DIVIDE, is realizable by the set of choices specified in the lexicon. Each Figure 8: Problem Decomposition Figure 9: Sub problem i solid line ....

S. Beale.'1997. Hunter-gatherer: Applying constraint satisfaction, branch-and-bound and solu- tion synthesis to computational semantics. Ph.D. Diss., Program in Language and In[ormation Technolo- gies, School of Computer Science, Carnegie Mellon University.

....3 Efficient Constraint based Processing The Mikrokosmos project utilizes an efficient, constraint directed control architecture called HunterGatherer (HG) Beale et al..1996] overviews how it enables semantic analysis to be performed in near linear time. Its use in generation is quite similar. Beale1997] describes HG in detail. Consider Figure 8, a representation of the constraint interactions present in a section of Figure 1. Each label, such as DIVIDE, is realizable by the set of choices specified in the lexicon. Each solid line represents an instance of one of the above constraint types. For ....

S. Beale. 1997. Hunter-gatherer: Applying constraint satisfaction, branch-and-bound and solution synthesis to computational semantics. Ph.D. Diss., Program in Language and Information Technologies, School of Computer Science, Carnegie Mellon University.

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Stephen Beale. 1996. HUNTER-GATHERER: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Natural Language Semantics. Technical Report, MCCS-96-289, Computing Research Lab, New Mexico State Univ.

....the formalism of typed feature structures, both cases are of type Co occurrence as defined below: Co occurrence = base: Entry, collocate: Entry, freq: Frequency] 3. 1 Processing of Syntagmatic Relations We utilize an efficient constraint based control mechanism called Hunter Gatherer (HG) (Beale, 1997). HG allows us to mark certain compositions as being dependent on each other and then forget about them. Thus, once we have two lexicon entries that we know go together, HG will ensure that they do. HG also gives preference to co occurring compositions. In analysis, meaning representations ....

S. Beale. 1997. HUNTER-GATHERER: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Computational Semantics.

.... Three Search Techniques Integrated for Natural Language Semantics Stephen Beale, Sergei Nirenburg and Kavi Mahesh Computing Research Laboratory Box 30001 New Mexico State University Las Cruces, New Mexico 88003 sb,sergei,mahesh crl.nmsu.edu In Proc. AAAI 96, Portland, OR. 1996 Abstract This work 1 integrates three related AI search techniques constraint satisfaction, ....

....search space most likely to contain acceptable answers. Best first search (see, among many others, Charniak et al. 1987) is an example of how to use heuristics. The hunting techniques applied in this research are most closely related to the field of constraint satisfaction problems (CSP) (Beale 1996) overviews this field and (Tsang 1993) covers it in depth. Further references include (Mackworth 1977) Mackworth Freuder 1985) and (Mohr Henderson 1986) Gathering has been studied much less in AI. Most AI problems are content with a single acceptable answer. Heuristic search methods ....

[Article contains additional citation context not shown here]

Beale, S. 1996. Hunter-Gatherer: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Natural Language Semantics, Technical Report, MCCS-96-289, Computing Research Lab, New Mexico State Univ.

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Steve Beale. 1997. Hunter-Gatherer: applying constraint satisfaction, branch-and-bound and solution synthesis to computational semantics. PhD thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.

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Beale, Stephen (1997). Hunter-Gatherer: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Computational Semantics. (upcoming) Ph.D. diss., Program in Language and Information Technologies, School of Computer Science, Carnegie Mellon University.

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Beale, S. 1996. Hunter-Gatherer: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Natural Language Semantics, Technical Report, MCCS-96-289, Computing Research Lab, New Mexico State Univ.

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