9,217 research outputs found
Manual Annotation of Translational Equivalence: The Blinker Project
Bilingual annotators were paid to link roughly sixteen thousand corresponding
words between on-line versions of the Bible in modern French and modern
English. These annotations are freely available to the research community from
http://www.cis.upenn.edu/~melamed . The annotations can be used for several
purposes. First, they can be used as a standard data set for developing and
testing translation lexicons and statistical translation models. Second,
researchers in lexical semantics will be able to mine the annotations for
insights about cross-linguistic lexicalization patterns. Third, the annotations
can be used in research into certain recently proposed methods for monolingual
word-sense disambiguation. This paper describes the annotated texts, the
specially-designed annotation tool, and the strategies employed to increase the
consistency of the annotations. The annotation process was repeated five times
by different annotators. Inter-annotator agreement rates indicate that the
annotations are reasonably reliable and that the method is easy to replicate
Models of Co-occurrence
A model of co-occurrence in bitext is a boolean predicate that indicates
whether a given pair of word tokens co-occur in corresponding regions of the
bitext space. Co-occurrence is a precondition for the possibility that two
tokens might be mutual translations. Models of co-occurrence are the glue that
binds methods for mapping bitext correspondence with methods for estimating
translation models into an integrated system for exploiting parallel texts.
Different models of co-occurrence are possible, depending on the kind of bitext
map that is available, the language-specific information that is available, and
the assumptions made about the nature of translational equivalence. Although
most statistical translation models are based on models of co-occurrence,
modeling co-occurrence correctly is more difficult than it may at first appear
Automatic Discovery of Non-Compositional Compounds in Parallel Data
Automatic segmentation of text into minimal content-bearing units is an
unsolved problem even for languages like English. Spaces between words offer an
easy first approximation, but this approximation is not good enough for machine
translation (MT), where many word sequences are not translated word-for-word.
This paper presents an efficient automatic method for discovering sequences of
words that are translated as a unit. The method proceeds by comparing pairs of
statistical translation models induced from parallel texts in two languages. It
can discover hundreds of non-compositional compounds on each iteration, and
constructs longer compounds out of shorter ones. Objective evaluation on a
simple machine translation task has shown the method's potential to improve the
quality of MT output. The method makes few assumptions about the data, so it
can be applied to parallel data other than parallel texts, such as word
spellings and pronunciations.Comment: 12 pages; uses natbib.sty, here.st
Automatic Construction of Clean Broad-Coverage Translation Lexicons
Word-level translational equivalences can be extracted from parallel texts by
surprisingly simple statistical techniques. However, these techniques are
easily fooled by {\em indirect associations} --- pairs of unrelated words whose
statistical properties resemble those of mutual translations. Indirect
associations pollute the resulting translation lexicons, drastically reducing
their precision. This paper presents an iterative lexicon cleaning method. On
each iteration, most of the remaining incorrect lexicon entries are filtered
out, without significant degradation in recall. This lexicon cleaning technique
can produce translation lexicons with recall and precision both exceeding 90\%,
as well as dictionary-sized translation lexicons that are over 99\% correct.Comment: PostScript file, 10 pages. To appear in Proceedings of AMTA-9
Word-to-Word Models of Translational Equivalence
Parallel texts (bitexts) have properties that distinguish them from other
kinds of parallel data. First, most words translate to only one other word.
Second, bitext correspondence is noisy. This article presents methods for
biasing statistical translation models to reflect these properties. Analysis of
the expected behavior of these biases in the presence of sparse data predicts
that they will result in more accurate models. The prediction is confirmed by
evaluation with respect to a gold standard -- translation models that are
biased in this fashion are significantly more accurate than a baseline
knowledge-poor model. This article also shows how a statistical translation
model can take advantage of various kinds of pre-existing knowledge that might
be available about particular language pairs. Even the simplest kinds of
language-specific knowledge, such as the distinction between content words and
function words, is shown to reliably boost translation model performance on
some tasks. Statistical models that are informed by pre-existing knowledge
about the model domain combine the best of both the rationalist and empiricist
traditions
Fringe field simulations of a non-scaling FFAG accelerator
Fixed-field Alternating Gradient (FFAG) accelerators offer the potential of
high-quality, moderate energy ion beams at low cost. Modeling of these
structures is challenging with conventional beam tracking codes because of the
large radial excursions of the beam and the significance of fringe field
effects. Numerous tune resonances are crossed during the acceleration, which
would lead to beam instability and loss in a storage ring. In a non-scaling
FFAG, the hope is that these resonances can be crossed sufficiently rapidly to
prevent beam loss. Simulations are required to see if this is indeed the case.
Here we simulate a non-scaling FFAG which accelerates protons from 31 to 250
MeV. We assume only that the bending magnets have mid-plane symmetry, with
specified vertical bending field in the mid-plane (y=0). The magnetic field can
be obtained everywhere using a power series expansion, and we develop
mathematical tools for calculating this expansion to arbitrary order when the
longitudinal field profile is given by an Enge function. We compare the use of
a conventional hard-edge fringe with a more accurate, soft-edge fringe field
model. The tune 1/3 resonance is the strongest, and crossing it in the
hard-edge fringe model results in a 21% loss of the beam. Using the soft-edge
fringe model the beam loss is less than 6%.Comment: 12 pages; 12 figure
Embedding multidimensional grids into optimal hypercubes
Let and be graphs, with , and a one to one map of their vertices. Let , where is the distance
between vertices and of . Now let = , over all such maps .
The parameter is a generalization of the classic and well studied
"bandwidth" of , defined as , where is the path on
points and . Let
be the -dimensional grid graph with integer values through in
the 'th coordinate. In this paper, we study in the case when and is the hypercube
of dimension , the hypercube of
smallest dimension having at least as many points as . Our main result is
that
provided for each . For such , the bound
improves on the previous best upper bound . Our methods include
an application of Knuth's result on two-way rounding and of the existence of
spanning regular cyclic caterpillars in the hypercube.Comment: 47 pages, 8 figure
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