17,029 research outputs found
Precise n-gram Probabilities from Stochastic Context-free Grammars
We present an algorithm for computing n-gram probabilities from stochastic
context-free grammars, a procedure that can alleviate some of the standard
problems associated with n-grams (estimation from sparse data, lack of
linguistic structure, among others). The method operates via the computation of
substring expectations, which in turn is accomplished by solving systems of
linear equations derived from the grammar. We discuss efficient implementation
of the algorithm and report our practical experience with it.Comment: 12 pages, to appear in ACL-9
On non-recursive trade-offs between finite-turn pushdown automata
It is shown that between one-turn pushdown automata (1-turn PDAs) and deterministic finite automata (DFAs) there will be savings concerning the size of description not bounded by any recursive function, so-called non-recursive tradeoffs. Considering the number of turns of the stack height as a consumable resource of PDAs, we can show the existence of non-recursive trade-offs between PDAs performing k+ 1 turns and k turns for k >= 1. Furthermore, non-recursive trade-offs are shown between arbitrary PDAs and PDAs which perform only a finite number of turns. Finally, several decidability questions are shown to be undecidable and not semidecidable
Context-Free Path Querying by Matrix Multiplication
Graph data models are widely used in many areas, for example, bioinformatics,
graph databases. In these areas, it is often required to process queries for
large graphs. Some of the most common graph queries are navigational queries.
The result of query evaluation is a set of implicit relations between nodes of
the graph, i.e. paths in the graph. A natural way to specify these relations is
by specifying paths using formal grammars over the alphabet of edge labels. An
answer to a context-free path query in this approach is usually a set of
triples (A, m, n) such that there is a path from the node m to the node n,
whose labeling is derived from a non-terminal A of the given context-free
grammar. This type of queries is evaluated using the relational query
semantics. Another example of path query semantics is the single-path query
semantics which requires presenting a single path from the node m to the node
n, whose labeling is derived from a non-terminal A for all triples (A, m, n)
evaluated using the relational query semantics. There is a number of algorithms
for query evaluation which use these semantics but all of them perform poorly
on large graphs. One of the most common technique for efficient big data
processing is the use of a graphics processing unit (GPU) to perform
computations, but these algorithms do not allow to use this technique
efficiently. In this paper, we show how the context-free path query evaluation
using these query semantics can be reduced to the calculation of the matrix
transitive closure. Also, we propose an algorithm for context-free path query
evaluation which uses relational query semantics and is based on matrix
operations that make it possible to speed up computations by using a GPU.Comment: 9 pages, 11 figures, 2 table
Search and Result Presentation in Scientific Workflow Repositories
We study the problem of searching a repository of complex hierarchical
workflows whose component modules, both composite and atomic, have been
annotated with keywords. Since keyword search does not use the graph structure
of a workflow, we develop a model of workflows using context-free bag grammars.
We then give efficient polynomial-time algorithms that, given a workflow and a
keyword query, determine whether some execution of the workflow matches the
query. Based on these algorithms we develop a search and ranking solution that
efficiently retrieves the top-k grammars from a repository. Finally, we propose
a novel result presentation method for grammars matching a keyword query, based
on representative parse-trees. The effectiveness of our approach is validated
through an extensive experimental evaluation
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