15,422 research outputs found
Efficient Analysis of Complex Diagrams using Constraint-Based Parsing
This paper describes substantial advances in the analysis (parsing) of
diagrams using constraint grammars. The addition of set types to the grammar
and spatial indexing of the data make it possible to efficiently parse real
diagrams of substantial complexity. The system is probably the first to
demonstrate efficient diagram parsing using grammars that easily be retargeted
to other domains. The work assumes that the diagrams are available as a flat
collection of graphics primitives: lines, polygons, circles, Bezier curves and
text. This is appropriate for future electronic documents or for vectorized
diagrams converted from scanned images. The classes of diagrams that we have
analyzed include x,y data graphs and genetic diagrams drawn from the biological
literature, as well as finite state automata diagrams (states and arcs). As an
example, parsing a four-part data graph composed of 133 primitives required 35
sec using Macintosh Common Lisp on a Macintosh Quadra 700.Comment: 9 pages, Postscript, no fonts, compressed, uuencoded. Composed in
MSWord 5.1a for the Mac. To appear in ICDAR '95. Other versions at
ftp://ftp.ccs.neu.edu/pub/people/futrell
Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
Latent tree learning models represent sentences by composing their words
according to an induced parse tree, all based on a downstream task. These
models often outperform baselines which use (externally provided) syntax trees
to drive the composition order. This work contributes (a) a new latent tree
learning model based on shift-reduce parsing, with competitive downstream
performance and non-trivial induced trees, and (b) an analysis of the trees
learned by our shift-reduce model and by a chart-based model.Comment: ACL 2018 workshop on Relevance of Linguistic Structure in Neural
Architectures for NL
AT-GIS: highly parallel spatial query processing with associative transducers
Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. ATGIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers(ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3Ă the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10Ă for aggregation queries
Using dialogue to learn math in the LeActiveMath project
We describe a tutorial dialogue system under development that assists students in learning how to differentiate equations. The system uses deep natural language understanding and generation to both interpret students â utterances and automatically generate a response that is both mathematically correct and adapted pedagogically and linguistically to the local dialogue context. A domain reasoner provides the necessary knowledge about how students should approach math problems as well as their (in)correctness, while a dialogue manager directs pedagogical strategies and keeps track of what needs to be done to keep the dialogue moving along.
An Investigation into Animating Plant Structures within Real-time Constraints
This paper is an analysis of current developments in rendering botanical structures for scientic and entertainment purposes with a focus on visualising growth. The choices of practical investigations produce a novel approach for parallel parsing of difficult bracketed L-Systems, based upon the work of Lipp, Wonka and Wimmer (2010). Alongside this is a general overview of the issues involved when looking at growing systems, technical details involving programming for the Graphics Processing Unit (GPU) and other possible solutions for further work that also could achieve the project's goals
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