Multilingual Information Retrieval

Peters, Carol; Braschler, Martín; Clough, Paul

doi:10.1007/978-3-642-23008-0

Cited by 71 publications

(62 citation statements)

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“…However, most of the techniques described can be applied to document translation as well. A detailed overview of CLIR can be found in Nie [106], Peters et al [107], or Zhou et al [108].…”

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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“…However, most of the techniques described can be applied to document translation as well. A detailed overview of CLIR can be found in Nie [106], Peters et al [107], or Zhou et al [108].…”

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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“…Amharic and Arabic documents are pre-processed separately by performing tokenization, normalization, stop word removal, punctuation removal and stemming. Figure 3.1 shows the general architecture of the system, which is adopted from C. Peters et al [25]. Bi-lingual dictionary, which includes the list of Amharic and Arabic translated words is constructed manually and is used to translate Amharic queries to Arabic queries.…”

Section: Methodsmentioning

confidence: 99%

Dictionary Based Amharic-Arabic Cross Language Information Retrieval

Shashirekha¹,

Gashaw²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…The most obvious way to compare documents written in different languages is to use machine translation and perform monolingual similarity (see Peters & Braschler, 2012;Potthast, Barrón-Cedeño, Stein, & Rosso, 2011). One can use free tools such as Moses (Koehn et al, 2007) or translation services, such as Google Translate (https://translate.google.com/).…”

Section: Translation and Dictionary Based Approachesmentioning

confidence: 99%

News Across Languages - Cross-Lingual Document Similarity and Event Tracking

Rupnik¹,

Muhič²,

Leban³

et al. 2016

jair

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In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.

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Multilingual Information Retrieval

Cited by 71 publications

References 0 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

Dictionary Based Amharic-Arabic Cross Language Information Retrieval

News Across Languages - Cross-Lingual Document Similarity and Event Tracking

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