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A knowledge-lite approach to word alignment
Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology. (NLPLab, HCS)
Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology. (NLPLab, IDA)
2000 (English)In: Parallel Text Processing: Alignment and Use of Translation Corpora / [ed] Jean Veronis, Dordrecht, The Netherlands: Kluwer Academic Publishers, 2000, 97-116 p.Chapter in book (Other academic)
Abstract [en]

The most promising approach to word alignment is to combine statistical methods with non-statistical information sources. Some of the proposed non-statistical sources, including bilingual dictionaries, POS-taggers and lemmatizers, rely on considerable linguistic knowledge, while other knowledge-lite sources such as cognate heuristics and word order heuristics can be implemented relatively easy. While knowledge-heavy sources might be expected to give better performance, knowledge-lite systems are easier to port to new language pairs and text types, and they can give sufficiently good results for many purposes, e.g. if the output is to be used by a human user for the creation of a complete word-aligned bitext. In this paper we describe the current status of the Linköping Word Aligner (LWA), which combines the use of statistical measures of co-occurrence with four knowledge-lite modules for (i)) word categorization, (ii) morphological variation, (iii) word order, and (iv) phrase recognition. We demonstrate the portability of the system (from English-Swedish texts to French-English texts) and present results for these two language-pairs. Finally, we will report observations from an error analysis of system output, and identify the major strengths and weaknesses of the system.

Place, publisher, year, edition, pages
Dordrecht, The Netherlands: Kluwer Academic Publishers, 2000. 97-116 p.
Series
Text, Speech and Language Technology, ISSN 1386-291X ; 13
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:liu:diva-64329DOI: 10.1007/978-94-017-2535-4_5ISBN: 978-90-481-5555-2 (print)ISBN: 978-94-017-2535-4 (print)OAI: oai:DiVA.org:liu-64329DiVA: diva2:389405
Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2016-07-08Bibliographically approved

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Ahrenberg, LarsMerkel, Magnus

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf