24,447 research outputs found
Translation Memory Retrieval Methods
Translation Memory (TM) systems are one of the most widely used translation
technologies. An important part of TM systems is the matching algorithm that
determines what translations get retrieved from the bank of available
translations to assist the human translator. Although detailed accounts of the
matching algorithms used in commercial systems can't be found in the
literature, it is widely believed that edit distance algorithms are used. This
paper investigates and evaluates the use of several matching algorithms,
including the edit distance algorithm that is believed to be at the heart of
most modern commercial TM systems. This paper presents results showing how well
various matching algorithms correlate with human judgments of helpfulness
(collected via crowdsourcing with Amazon's Mechanical Turk). A new algorithm
based on weighted n-gram precision that can be adjusted for translator length
preferences consistently returns translations judged to be most helpful by
translators for multiple domains and language pairs.Comment: 9 pages, 6 tables, 3 figures; appeared in Proceedings of the 14th
Conference of the European Chapter of the Association for Computational
Linguistics, April 201
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
Many important forms of data are stored digitally in XML format. Errors can
occur in the textual content of the data in the fields of the XML. Fixing these
errors manually is time-consuming and expensive, especially for large amounts
of data. There is increasing interest in the research, development, and use of
automated techniques for assisting with data cleaning. Electronic dictionaries
are an important form of data frequently stored in XML format that frequently
have errors introduced through a mixture of manual typographical entry errors
and optical character recognition errors. In this paper we describe methods for
flagging statistical anomalies as likely errors in electronic dictionaries
stored in XML format. We describe six systems based on different sources of
information. The systems detect errors using various signals in the data
including uncommon characters, text length, character-based language models,
word-based language models, tied-field length ratios, and tied-field
transliteration models. Four of the systems detect errors based on expectations
automatically inferred from content within elements of a single field type. We
call these single-field systems. Two of the systems detect errors based on
correspondence expectations automatically inferred from content within elements
of multiple related field types. We call these tied-field systems. For each
system, we provide an intuitive analysis of the type of error that it is
successful at detecting. Finally, we describe two larger-scale evaluations
using crowdsourcing with Amazon's Mechanical Turk platform and using the
annotations of a domain expert. The evaluations consistently show that the
systems are useful for improving the efficiency with which errors in XML
electronic dictionaries can be detected.Comment: 8 pages, 4 figures, 5 tables; published in Proceedings of the 2016
IEEE Tenth International Conference on Semantic Computing (ICSC), Laguna
Hills, CA, USA, pages 79-86, February 201
Short-time Fourier transform laser Doppler holography
We report a demonstration of laser Doppler holography at a sustained
acquisition rate of 250 Hz on a 1 Megapixel complementary
metal-oxide-semiconductor (CMOS) sensor array and image display at 10 Hz frame
rate. The holograms are optically acquired in off-axis configuration, with a
frequency-shifted reference beam. Wide-field imaging of optical fluctuations in
a 250 Hz frequency band is achieved by turning time-domain samplings to the
dual domain via short-time temporal Fourier transformation. The measurement
band can be positioned freely within the low radio-frequency spectrum by tuning
the frequency of the reference beam in real-time. Video-rate image rendering is
achieved by streamline image processing with commodity computer graphics
hardware. This experimental scheme is validated by a non-contact vibrometry
experiment
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
We present BPEmb, a collection of pre-trained subword unit embeddings in 275
languages, based on Byte-Pair Encoding (BPE). In an evaluation using
fine-grained entity typing as testbed, BPEmb performs competitively, and for
some languages bet- ter than alternative subword approaches, while requiring
vastly fewer resources and no tokenization. BPEmb is available at
https://github.com/bheinzerling/bpem
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