scholarly journals Exploiting the Web as Parallel Corpora for Cross-Language Information Retrieval

2003 ◽  
pp. 218-239
Author(s):  
Jian-Yun Nie ◽  
Jiang Chen
Keyword(s):  
Information Retrieval ◽  
Parallel Corpora ◽  
Cross Language Information Retrieval ◽  
Cross Language ◽  
The Web

scholarly journals Embedding Web-Based Statistical Translation Models in Cross-Language Information Retrieval

2003 ◽  
Vol 29 (3) ◽  
pp. 381-419 ◽  
Author(s):  
Wessel Kraaij ◽  
Jian-Yun Nie ◽  
Michel Simard
Keyword(s):  
Information Retrieval ◽  
Low Cost ◽  
Standard Test ◽  
Query Translation ◽  
Web Based ◽  
Parallel Corpora ◽  
Cross Language Information Retrieval ◽  
Cross Language ◽  
Parallel Texts ◽  
The Web

Although more and more language pairs are covered by machine translation (MT) services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application that needs translation functionality of a relatively low level of sophistication, since current models for information retrieval (IR) are still based on a bag of words. The Web provides a vast resource for the automatic construction of parallel corpora that can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this article, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.

Cross-language Ontology Learning

2011 ◽  
pp. 272-297
Author(s):  
Hans Hjelm ◽  
Martin Volk
Keyword(s):  
Information Retrieval ◽  
Learning Task ◽  
Formal Ontology ◽  
Ontology Learning ◽  
Lexical Knowledge ◽  
Automatic Learning ◽  
Parallel Corpora ◽  
Cross Language Information Retrieval ◽  
Language Data ◽  
Cross Language

A formal ontology does not contain lexical knowledge; it is by nature language-independent. Mappings can be added between the ontology and, arbitrarily, many lexica in any number of languages. The result of this operation is what is here referred to as a cross-language ontology. A cross-language ontology can be a useful resource for machine translation or cross-language information retrieval. This chapter focuses on ways of automatically building an ontology by exploiting cross-language information from parallel corpora. The goal is to improve the automatic learning results compared to learning an ontology from resources in a single language. The authors present a framework for cross-language ontology learning, providing a setting in which cross-language evidence (data) can be integrated and quantified. The aim is to investigate the following question: Can cross-language data teach us more than data from a single language for the ontology learning task?

Improving Cross-Language Information Retrieval by Harnessing the Social Web

2010 ◽  
pp. 277-295
Author(s):  
Diana Irina Tanase ◽  
Epaminondas Kapetanios
Keyword(s):  
Information Retrieval ◽  
Large Scale ◽  
Social Web ◽  
Web Based ◽  
Lexical Resource ◽  
Cross Language Information Retrieval ◽  
The Social ◽  
Current Context ◽  
Cross Language ◽  
The Web

Combining existing advancements in cross-language information retrieval (CLIR) with the new usercentered Web paradigm could allow tapping into Web-based multilingual clusters of language information that are rich, up-to-date in terms of language usage, that increase in size, and have the potential to cater for all languages. In this chapter, we set out to explore existing CLIR systems and their limitations, and we argue that in the current context of a widely adopted social Web, the future of large-scale CLIR and iCLIR systems is linked to the use of the Web as a lexical resource, as a distribution infrastructure, and as a channel of communication between users. Such a synergy will lead to systems that grow organically as more users with different linguistic skills join the network, and that improve in terms of language translations disambiguation and coverage.

Parallel sentence extraction to improve cross-language information retrieval from Wikipedia

2021 ◽  
pp. 016555152199275
Author(s):  
Juryong Cheon ◽  
Youngjoong Ko
Keyword(s):  
Information Retrieval ◽  
Language Resources ◽  
Query Translation ◽  
Factors Affecting ◽  
Parallel Corpora ◽  
Parallel Corpus ◽  
Bilingual Dictionary ◽  
Sentence Extraction ◽  
Cross Language Information Retrieval ◽  
Cross Language

Translation language resources, such as bilingual word lists and parallel corpora, are important factors affecting the effectiveness of cross-language information retrieval (CLIR) systems. In particular, when large domain-appropriate parallel corpora are not available, developing an effective CLIR system is particularly difficult. Furthermore, creating a large parallel corpus is costly and requires considerable effort. Therefore, we here demonstrate the construction of parallel corpora from Wikipedia as well as improved query translation, wherein the queries are used for a CLIR system. To do so, we first constructed a bilingual dictionary, termed WikiDic. Then, we evaluated individual language resources and combinations of them in terms of their ability to extract parallel sentences; the combinations of our proposed WikiDic with the translation probability from the Web’s bilingual example sentence pairs and WikiDic was found to be best suited to parallel sentence extraction. Finally, to evaluate the parallel corpus generated from this best combination of language resources, we compared its performance in query translation for CLIR to that of a manually created English–Korean parallel corpus. As a result, the corpus generated by our proposed method achieved a better performance than did the manually created corpus, thus demonstrating the effectiveness of the proposed method for automatic parallel corpus extraction. Not only can the method demonstrated herein be used to inform the construction of other parallel corpora from language resources that are readily available, but also, the parallel sentence extraction method will naturally improve as Wikipedia continues to be used and its content develops.

Cross-Language Information Retrieval on the Web

2011 ◽  
pp. 704-719 ◽  
Author(s):  
María-Dolores Olvera-Lobo
Keyword(s):  
Information Retrieval ◽  
New Technologies ◽  
Public Information ◽  
Query Languages ◽  
Point Of View ◽  
Retrieval Models ◽  
Cross Language Information Retrieval ◽  
The World ◽  
Cross Language ◽  
The Web

The Web stands today as the world´s largest source of public information. Its magnitude can also be perceived as a drawback in a certain sense, however: nowadays there is a generalized problem in retrieving documents that may be written in any language, but through queries expressed in a single source language. And although Information Retrieval (IR) depends on the availability of digital collections, this key aspect is no longer the only concern. It is time for the multicultural society of Internet to make use of new technologies such as Cross-Language Information Retrieval (CLIR). Whereas classical IR is a field that embraces retrieval models, evaluation, query languages and document indexing involving “small” collections of documents, modern IR tends to focus on Internet search engines, mark-up languages, multimedia contents, the distribution of collections, user interaction and multilingual systems. Thus, CLIR may border on work in the following fields: information retrieval, natural language processing, machine translation and abstracting, speech processing, the interpretation of document images, and human-computer interaction. “Given a query in any medium and any language, select relevant items from a multilingual multimedia collection which can be in any medium and any language, and present them in the style or order most likely to be useful to the querier, with identical or near identical objects in different media or languages appropriately identified” (Hull & Oard, 1997). This sentence sums up the main objective of CLIR, acknowledged as an independent research subfield roughly a decade ago, so that at present a number of international CLIR conferences take place in the world. The most importantof these are TREC (Text REtrieval Conference) in the US; NTCIR (NII-NACSIS Test Collection for IR Systems) in Asia; and CLEF (Cross-Language Evaluation Forum) in Europe. This chapter attempts to characterize the scenario of Cross-Language Information Retrieval as a domain, with special attention to the Web as a resource for multilingual research. The authors also manifest their point of view about some major directions for CLIR research in the future.

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