• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 138,497
Next 10 →

Building and Using Parallel Texts. The

by Joel Martin, Rada Mihalcea, Ted Pedersen
"... This paper presents the task definition, resources, participating systems, and comparative results for the shared task on word alignment, which was organized ..."
Abstract - Add to MetaCart
This paper presents the task definition, resources, participating systems, and comparative results for the shared task on word alignment, which was organized

Development of Circulating Support Environment of Multilingual Medical Communication using Parallel Texts for

by Mai Miyabe, Taku Fukushima, Takashi Yoshino, Aguri Shigeno - Foreign Patients”, International Conference on Health and Medical Informatics (ICHMI 2010), World Academy of Science, Engineering and Technology – WASET
"... Abstract—The need for multilingual communication in Japan has increased due to an increase in the number of foreigners in the country. When people communicate in their nonnative language, the differences in language prevent mutual understanding among the communicating individuals. In the medical fie ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
&A function. Users can operate M3 using a touch screen and receive text-based support. In addition, M3 uses accurate translation tools called parallel texts to facilitate reliable communication through conversations between the hospital staff and the patients. However, if there is no parallel text

Parallel Networks that Learn to Pronounce English Text

by Terrence J. Sejnowski, Charles R. Rosenberg - COMPLEX SYSTEMS , 1987
"... This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed h ..."
Abstract - Cited by 549 (5 self) - Add to MetaCart
This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed

Dictionary Acquisition using Parallel Text and Co-occurrence Statistics

by Chris Biemann, Uwe Quasthoff
"... We present a simple and efficient approach for deriving bilingual dic-tionaries from sentence-aligned par-allel text by extending the notion of co-occurrences to a cross-lingual setting. Dictionaries are evaluated against gold standards and manu-ally; the analysis accounts for fre-quency and corpus ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
We present a simple and efficient approach for deriving bilingual dic-tionaries from sentence-aligned par-allel text by extending the notion of co-occurrences to a cross-lingual setting. Dictionaries are evaluated against gold standards and manu-ally; the analysis accounts for fre-quency and corpus

Europarl: A Parallel Corpus for Statistical Machine Translation

by Philipp Koehn
"... We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web 1. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine translat ..."
Abstract - Cited by 519 (1 self) - Add to MetaCart
We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web 1. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine

Proceedings of the ACL Workshop on Building and Using Parallel Texts, pages 183--190,

by Ann Arbor June, Declan Groves - In Proceedings of the Workshop on Building and Using Parallel Texts: Data-Driven Machine Translation and Beyond, ACL 2005, Ann Arbor , 2005
"... Way and Gough, 2005) provide an indepth comparison of their Example-Based Machine Translation (EBMT) system with a Statistical Machine Translation (SMT) system constructed from freely available tools. According to a wide variety of automatic evaluation metrics, they demonstrated that their EB ..."
Abstract - Add to MetaCart
Way and Gough, 2005) provide an indepth comparison of their Example-Based Machine Translation (EBMT) system with a Statistical Machine Translation (SMT) system constructed from freely available tools. According to a wide variety of automatic evaluation metrics, they demonstrated that their EBMT system outperformed the SMT system by a factor of two to one.

Text Classification using String Kernels

by Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Chris Watkins
"... We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguo ..."
Abstract - Cited by 495 (7 self) - Add to MetaCart
We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily

Text Chunking using Transformation-Based Learning

by Lance A. Ramshaw, Mitchell P. Marcus , 1995
"... Eric Brill introduced transformation-based learning and showed that it can do part-ofspeech tagging with fairly high accuracy. The same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive "baseNP" chunks. For ..."
Abstract - Cited by 523 (0 self) - Add to MetaCart
Eric Brill introduced transformation-based learning and showed that it can do part-ofspeech tagging with fairly high accuracy. The same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive "baseNP" chunks

Parallel Numerical Linear Algebra

by James W. Demmel, Michael T. Heath , Henk A. van der Vorst , 1993
"... We survey general techniques and open problems in numerical linear algebra on parallel architectures. We first discuss basic principles of parallel processing, describing the costs of basic operations on parallel machines, including general principles for constructing efficient algorithms. We illust ..."
Abstract - Cited by 773 (23 self) - Add to MetaCart
We survey general techniques and open problems in numerical linear algebra on parallel architectures. We first discuss basic principles of parallel processing, describing the costs of basic operations on parallel machines, including general principles for constructing efficient algorithms. We

An evaluation of statistical approaches to text categorization

by Yiming Yang - Journal of Information Retrieval , 1999
"... Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine th ..."
Abstract - Cited by 663 (22 self) - Add to MetaCart
Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine
Next 10 →
Results 1 - 10 of 138,497
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University