8,079 research outputs found
A syntactified direct translation model with linear-time decoding
Recent syntactic extensions of statistical translation models work with a synchronous context-free or tree-substitution grammar extracted from an automatically parsed parallel corpus. The decoders accompanying these extensions typically exceed quadratic time complexity. This paper extends the Direct Translation Model 2 (DTM2) with syntax while maintaining linear-time decoding. We employ a linear-time parsing algorithm based on an eager, incremental interpretation of Combinatory Categorial Grammar
(CCG). As every input word is processed, the local parsing decisions resolve ambiguity eagerly, by selecting a single
supertag–operator pair for extending the dependency parse incrementally. Alongside translation features extracted from
the derived parse tree, we explore syntactic features extracted from the incremental derivation process. Our empirical experiments show that our model significantly
outperforms the state-of-the art DTM2 system
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
From social networks to language modeling, the growing scale and importance
of graph data has driven the development of numerous new graph-parallel systems
(e.g., Pregel, GraphLab). By restricting the computation that can be expressed
and introducing new techniques to partition and distribute the graph, these
systems can efficiently execute iterative graph algorithms orders of magnitude
faster than more general data-parallel systems. However, the same restrictions
that enable the performance gains also make it difficult to express many of the
important stages in a typical graph-analytics pipeline: constructing the graph,
modifying its structure, or expressing computation that spans multiple graphs.
As a consequence, existing graph analytics pipelines compose graph-parallel and
data-parallel systems using external storage systems, leading to extensive data
movement and complicated programming model.
To address these challenges we introduce GraphX, a distributed graph
computation framework that unifies graph-parallel and data-parallel
computation. GraphX provides a small, core set of graph-parallel operators
expressive enough to implement the Pregel and PowerGraph abstractions, yet
simple enough to be cast in relational algebra. GraphX uses a collection of
query optimization techniques such as automatic join rewrites to efficiently
implement these graph-parallel operators. We evaluate GraphX on real-world
graphs and workloads and demonstrate that GraphX achieves comparable
performance as specialized graph computation systems, while outperforming them
in end-to-end graph pipelines. Moreover, GraphX achieves a balance between
expressiveness, performance, and ease of use
Concurrent Lexicalized Dependency Parsing: The ParseTalk Model
A grammar model for concurrent, object-oriented natural language parsing is
introduced. Complete lexical distribution of grammatical knowledge is achieved
building upon the head-oriented notions of valency and dependency, while
inheritance mechanisms are used to capture lexical generalizations. The
underlying concurrent computation model relies upon the actor paradigm. We
consider message passing protocols for establishing dependency relations and
ambiguity handling.Comment: 90kB, 7pages Postscrip
Multi-node approach for map data processing
OpenStreetMap (OSM) is a popular collaborative open-source project that offers free editable map across the whole world. However, this data often needs a further on-purpose processing to become the utmost valuable information to work with. That is why the main motivation of this paper is to propose a design for big data processing along with data mining leading to the obtaining of statistics with a focus on the detail of a traffic data as a result in order to create graphs representing a road network. To ensure our High-Performance Computing (HPC) platform routing algorithms work correctly, it is absolutely essential to prepare OSM data to be useful and applicable for above-mentioned graph, and to store this persistent data in both spatial database and HDF5 format.Web of Science8971049
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