1,483 research outputs found
Using Global Constraints and Reranking to Improve Cognates Detection
Global constraints and reranking have not been used in cognates detection
research to date. We propose methods for using global constraints by performing
rescoring of the score matrices produced by state of the art cognates detection
systems. Using global constraints to perform rescoring is complementary to
state of the art methods for performing cognates detection and results in
significant performance improvements beyond current state of the art
performance on publicly available datasets with different language pairs and
various conditions such as different levels of baseline state of the art
performance and different data size conditions, including with more realistic
large data size conditions than have been evaluated with in the past.Comment: 10 pages, 6 figures, 6 tables; published in the Proceedings of the
55th Annual Meeting of the Association for Computational Linguistics, pages
1983-1992, Vancouver, Canada, July 201
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
Private Sector Participation in the Water and Wastewater Services Industry
Countries introduce private sector participation into the water and wastewater utilities sector for a number of reasons. The introduction of a profit motive may increase efficiency as compared to public management of the water system, and private firms have been noted for customer service improvements. Financial considerations, including revenues from the sale of assets and reductions in the direct cost of providing water services, may also motivate governments to introduce private sector participation in this industry. However, because water is a basic human necessity, the introduction of private participation in this industry sector may raise social, economic, and national security concerns. Private participation in the global water and wastewater industry can take a number of forms including privatization, greenfield projects, concessions, leases, operation and management contracts, and outsourcing and most countries employ a mix of methods. A handful of European firms dominate trade and investment in the global water and wastewater utilities market.water, wastewater, environmental services, private sector participation, Public Economics,
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