Anchor text mining for translation extraction of query terms

Wen Hsiang Lu; Lee Feng Chein; Hsi Jian Lee

doi:10.1145/383952.384031

Anchor text mining for translation extraction of query terms

Wen Hsiang Lu, Lee Feng Chein, Hsi Jian Lee

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

This paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining the Web anchor texts. Some preliminary experiments are conducted on using 109,416 Web pages containing both Chinese and English anchor texts in their in-links to extract Chinese translations of 200 English queries selected from popular query terms in Taiwan. It is found that the effective translations of 75% of the popular query terms can be extracted, in which 87.2% cannot be obtained in common translation dictionaries.

Original language	English
Pages (from-to)	388-389
Number of pages	2
Journal	SIGIR Forum (ACM Special Interest Group on Information Retrieval)
DOIs	https://doi.org/10.1145/383952.384031
Publication status	Published - 2001
Event	24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - New Orleans, LA, United States Duration: 2001 Sep 9 → 2001 Sep 13

All Science Journal Classification (ASJC) codes

Management Information Systems
Hardware and Architecture

Access to Document

10.1145/383952.384031

Cite this

@article{994334a8903140519b7fec6d58e272e3,

title = "Anchor text mining for translation extraction of query terms",

abstract = "This paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining the Web anchor texts. Some preliminary experiments are conducted on using 109,416 Web pages containing both Chinese and English anchor texts in their in-links to extract Chinese translations of 200 English queries selected from popular query terms in Taiwan. It is found that the effective translations of 75% of the popular query terms can be extracted, in which 87.2% cannot be obtained in common translation dictionaries.",

author = "Lu, {Wen Hsiang} and Chein, {Lee Feng} and Lee, {Hsi Jian}",

year = "2001",

doi = "10.1145/383952.384031",

language = "English",

pages = "388--389",

journal = "SIGIR Forum (ACM Special Interest Group on Information Retrieval)",

issn = "0163-5840",

publisher = "Association for Computing Machinery (ACM)",

note = "24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ; Conference date: 09-09-2001 Through 13-09-2001",

}

TY - JOUR

T1 - Anchor text mining for translation extraction of query terms

AU - Lu, Wen Hsiang

AU - Chein, Lee Feng

AU - Lee, Hsi Jian

PY - 2001

Y1 - 2001

N2 - This paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining the Web anchor texts. Some preliminary experiments are conducted on using 109,416 Web pages containing both Chinese and English anchor texts in their in-links to extract Chinese translations of 200 English queries selected from popular query terms in Taiwan. It is found that the effective translations of 75% of the popular query terms can be extracted, in which 87.2% cannot be obtained in common translation dictionaries.

AB - This paper presents an approach to automatically extracting the bilingual translations of many Web query terms through mining the Web anchor texts. Some preliminary experiments are conducted on using 109,416 Web pages containing both Chinese and English anchor texts in their in-links to extract Chinese translations of 200 English queries selected from popular query terms in Taiwan. It is found that the effective translations of 75% of the popular query terms can be extracted, in which 87.2% cannot be obtained in common translation dictionaries.

UR - http://www.scopus.com/inward/record.url?scp=0034785486&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034785486&partnerID=8YFLogxK

U2 - 10.1145/383952.384031

DO - 10.1145/383952.384031

M3 - Conference article

AN - SCOPUS:0034785486

SP - 388

EP - 389

JO - SIGIR Forum (ACM Special Interest Group on Information Retrieval)

JF - SIGIR Forum (ACM Special Interest Group on Information Retrieval)

SN - 0163-5840

T2 - 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Y2 - 9 September 2001 through 13 September 2001

ER -

Anchor text mining for translation extraction of query terms

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this