Abstract
Bilingual web pages are widely used to mine translations of unknown terms. This study focused on an effective solution for obtaining relevant web pages, extracting translations with correct lexical boundaries, and ranking the translation candidates. This research adopted co-occurrence information to obtain the subject terms and then expanded the source query with the translation of the subject terms to collect effective bilingual search engine snippets. Afterwards, valid candidates were extracted from small-sized, noisy bilingual corpora using an improved frequency change measurement that combines adjacent information. This research developed a method that considers surface patterns, frequency–distance, and phonetic features to elect an appropriate translation. The experimental results revealed that the proposed method performed remarkably well for mining translations of unknown terms.
Highlights
The rapid development of Web 2.0 and constant expansion of the network size has led to a great increase in the amount of informational resources in multiple languages on the Internet
Cross-language information retrieval (CLIR) enables people to retrieve documents written in multiple languages through a single query
This study proposed an approach for mining the translations of unknown terms based on the web and it is an extension of our research team’s work
Summary
The rapid development of Web 2.0 and constant expansion of the network size has led to a great increase in the amount of informational resources in multiple languages on the Internet. Cross-language information retrieval (CLIR) enables people to retrieve documents written in multiple languages through a single query. Despite the rapid advancements in this field, CLIR still has a major choking point when a query involves the translation of unknown terms, which is known as an out-of-vocabulary (OOV) problem. If unknown terms are translated incorrectly, the performance of CLIR or other systems is greatly reduced. This research suggests a new approach for mining translations of web-based terms. It adopts co-occurrence information to expand the cross-linguistic terms so as to more effectively extract bilingual web snippets that are more relevant.
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