English-French Document Alignment Based on Keywords and
Statistical Translation

D 2016

English-French Document Alignment Based on Keywords and Statistical Translation

MEDVEĎ, Marek, Vojtěch KOVÁŘ and Miloš JAKUBÍČEK

Basic information

Original name

English-French Document Alignment Based on Keywords and Statistical Translation

Authors

MEDVEĎ, Marek (703 Slovakia, guarantor, belonging to the institution), Vojtěch KOVÁŘ (203 Czech Republic, belonging to the institution) and Miloš JAKUBÍČEK (203 Czech Republic, belonging to the institution)

Edition

Berlin, Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers, p. 728-732, 5 pp. 2016

Publisher

Association for Computational Linguistics

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/16:00088114

Organization unit

Faculty of Informatics

ISBN

978-1-945626-10-4

Keywords in English

bilingual document alignment

Změněno: 4/9/2016 22:05, RNDr. Miloš Jakubíček, Ph.D.

Abstract

V originále

In this paper we present our approach to the Bilingual Document Alignment Task (WMT16), where the main goal was to reach the best recall on extracting aligned pages within the provided data. Our approach consists of tree main parts: data preprocessing, keyword extraction and text pairs scoring based on keyword matching. For text preprocessing we use the TreeTagger pipeline that contains the Unitok tool (Michelfeit et al., 2014) for tokenization and the TreeTagger morphological analyzer (Schmid, 1994). After keywords extraction from the texts according TF-IDF scoring our system searches for comparable English-French pairs. Using a statistical dictionary created from a large English-French parallel corpus, the system is able to find comaparable documents. At the end this procedure is combined with the baseline algorithm and best one-to-one pairing is selected. The result reaches 91.6% recall on provided training data. After a deep error analysis (see section 5) the recall reached 97.4%.

Links

GA15-13277S, research and development project

Name: Hyperintensionální logika pro analýzu přirozeného jazyka

Investor: Czech Science Foundation

LM2015071, research and development project

Name: Jazyková výzkumná infrastruktura v České republice (Acronym: LINDAT-Clarin)

Investor: Ministry of Education, Youth and Sports of the CR

7F14047, research and development project

Name: Harvesting big text data for under-resourced languages (Acronym: HaBiT)

Investor: Ministry of Education, Youth and Sports of the CR

Citovat

MEDVEĎ, Marek, Vojtěch KOVÁŘ and Miloš JAKUBÍČEK. English-French Document Alignment Based on Keywords and Statistical Translation. Online. In Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers. Berlin: Association for Computational Linguistics, 2016, p. 728-732. ISBN 978-1-945626-10-4.

@inproceedings{1352922,
   author = {Medveď, Marek and Kovář, Vojtěch and Jakubíček, Miloš},
   address = {Berlin},
   booktitle = {Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers},
   keywords = {bilingual document alignment},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Berlin},
   isbn = {978-1-945626-10-4},
   pages = {728-732},
   publisher = {Association for Computational Linguistics},
   title = {English-French Document Alignment Based on Keywords and Statistical Translation},
   year = {2016}
}

TY  - JOUR
ID  - 1352922
AU  - Medveď, Marek - Kovář, Vojtěch - Jakubíček, Miloš
PY  - 2016
TI  - English-French Document Alignment Based on Keywords and Statistical Translation
PB  - Association for Computational Linguistics
CY  - Berlin
SN  - 9781945626104
KW  - bilingual document alignment
N2  - In this paper we present our approach to the Bilingual Document Alignment Task (WMT16), where the main goal was to reach the best recall on extracting aligned pages within the provided data. Our approach consists of tree main parts: data preprocessing, keyword extraction and text pairs scoring based on keyword matching. For text preprocessing we use the TreeTagger pipeline that contains the Unitok tool (Michelfeit et al., 2014) for tokenization and the TreeTagger morphological analyzer (Schmid, 1994). After keywords extraction from the texts according TF-IDF scoring our system searches for comparable English-French pairs. Using a statistical dictionary created from a large English-French parallel corpus, the system is able to find comaparable documents. At the end this procedure is combined with the baseline algorithm and best one-to-one pairing is selected. The result reaches 91.6% recall on provided training data. After a deep error analysis (see section 5) the recall reached 97.4%.
ER  -

MEDVEĎ, Marek, Vojtěch KOVÁŘ and Miloš JAKUBÍČEK. English-French Document Alignment Based on Keywords and Statistical Translation. Online. In \textit{Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers}. Berlin: Association for Computational Linguistics, 2016, p.~728-732. ISBN~978-1-945626-10-4.

Detailed Information on Publication Record