Phrasal translation and query expansion techniques for cross-language information retrieval

Ballesteros, Lisa; Croft, W. Bruce

doi:10.1145/258525.258540

Cited by 197 publications

(97 citation statements)

References 13 publications

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“…The high-weighted terms (assumed to be related) are then extracted from these documents and added to the original query. This technique was applied to CLIR e.g., by Ballesteros and Croft [117]. If performed also on the target side, it can mitigate the effects caused by picking wrong translation alternatives [see summary in 107].…”

Section: Cross-lingual Information Retrieval and Related Techniquesmentioning

confidence: 99%

Adaptation of machine translation for multilingual information retrieval in the medical domain

Pecina¹,

Dušek²,

Goeuriot³

et al. 2014

Artificial Intelligence in Medicine

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Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR.Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets.Results. The search query translation results achieved in our experiments are outstanding -our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results.Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance -better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions.

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Section: Cross-lingual Information Retrieval and Related Techniquesmentioning

confidence: 99%

Adaptation of machine translation for multilingual information retrieval in the medical domain

Pecina¹,

Dušek²,

Goeuriot³

et al. 2014

Artificial Intelligence in Medicine

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show abstract

“…Following the research reported by Ballesteros and Croft [1] on the use of a local feedback, adding terms that emphasize query concepts in the post and pre-translation phases, improves precision and recall. This combined method is supposed to reduce the ambiguity by de-emphasizing irrelevant terms added by translation and will improve precision and recall of an information retrieval.…”

Section: Combining Different Approachesmentioning

confidence: 96%

“…Query expansion has proved its effectiveness in the performance of an information retrieval [1], [3]. We use an approach of combined automatic query expansion before and after translation, with an extraction of expansion terms through the following techniques: Relevance feedback, with the selection of best terms, domain-based feedback with the extraction of domain keywords to add to the original query and thesaurus-based expansion with a retrieval of synonyms from a monolingual thesaurus.…”

Section: Query Expansion In Clirmentioning

confidence: 99%

Exploiting Thesauri and Hierarchical Categories in Cross-Language Information Retrieval

Sadat

Yoshikawa

Uemura

2002

Text, Speech and Dialogue

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Abstract. As Internet resources become accessible to more and more countries, there is a need to develop efficient methods for information retrieval across languages. In the present paper, we focus on query expansion techniques to improve the effectiveness of an information retrieval. A combination to a dictionary-based translation and statistical-based disambiguation is indispensable to overcome translation's ambiguity. We propose a model using multiple sources for query reformulation and expansion to select expansion terms and retrieve information needed by a user. Relevance feedback, thesaurus-based expansion, as well as a new feedback strategy, based on the extraction of domain keywords to expand user's query, are introduced and evaluated. We evaluated the effectiveness of the proposed combined method, by an application to a FrenchEnglish Information Retrieval.

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“…The Pre-translation Query Expansion component takes a search query and sends it to the local document collection to perform a search. To achieve higher efficiency, our approach followed the local feedback method [2]. The Query Translation component is responsible for translating search queries in the source language into the target language.…”

Section: Proposed Approach and System Prototypementioning

confidence: 99%

Supporting Multilingual Information Retrieval in Web Applications: An English-Chinese Web Portal Experiment

Qin

Zhou

Chau

et al. 2003

Digital Libraries: Technology and Management of Indigenous Knowledge for Global Access

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Abstract. Cross-language information retrieval (CLIR) and multilingual information retrieval (MLIR) techniques have been widely studied, but they are not often applied to and evaluated for Web applications. In this paper, we present our research in developing and evaluating a multilingual English-Chinese Web portal in the business domain. A dictionary-based approach has been adopted that combines phrasal translation, co-occurrence analysis, and pre-and post-translation query expansion. The approach was evaluated by domain experts and the results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision when compared with simple word-by-word translation.

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Phrasal translation and query expansion techniques for cross-language information retrieval

Cited by 197 publications

References 13 publications

Adaptation of machine translation for multilingual information retrieval in the medical domain

Adaptation of machine translation for multilingual information retrieval in the medical domain

Exploiting Thesauri and Hierarchical Categories in Cross-Language Information Retrieval

Supporting Multilingual Information Retrieval in Web Applications: An English-Chinese Web Portal Experiment

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