Transitive probabilistic CLIR models.

Transitive translation could be a useful technique to enlarge the number of supported language pairs for a cross-language information retrieval (CLIR) system in a cost-effective manner. The paper describes several setups for transitive translation based on probabilistic translation models. The transitive CLIR models were evaluated on the CLEF test collection and yielded a retrieval effectiveness\ud up to 83% of monolingual performance, which is significantly better than a baseline using the synonym operator

Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which 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 which 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 paper, 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.Comment: 37 page

Searching to Translate and Translating to Search: When Information Retrieval Meets Machine Translation

With the adoption of web services in daily life, people have access to tremendous amounts of information, beyond any human's reading and comprehension capabilities. As a result, search technologies have become a fundamental tool for accessing information. Furthermore, the web contains information in multiple languages, introducing another barrier between people and information. Therefore, search technologies need to handle content written in multiple languages, which requires techniques to account for the linguistic differences. Information Retrieval (IR) is the study of search techniques, in which the task is to find material relevant to a given information need. Cross-Language Information Retrieval (CLIR) is a special case of IR when the search takes place in a multi-lingual collection. Of course, it is not helpful to retrieve content in languages the user cannot understand. Machine Translation (MT) studies the translation of text from one language into another efficiently (within a reasonable amount of time) and effectively (fluent and retaining the original meaning), which helps people understand what is being written, regardless of the source language. Putting these together, we observe that search and translation technologies are part of an important user application, calling for a better integration of search (IR) and translation (MT), since these two technologies need to work together to produce high-quality output. In this dissertation, the main goal is to build better connections between IR and MT, for which we present solutions to two problems: Searching to translate explores approximate search techniques for extracting bilingual data from multilingual Wikipedia collections to train better translation models. Translating to search explores the integration of a modern statistical MT system into the cross-language search processes. In both cases, our best-performing approach yielded improvements over strong baselines for a variety of language pairs. Finally, we propose a general architecture, in which various components of IR and MT systems can be connected together into a feedback loop, with potential improvements to both search and translation tasks. We hope that the ideas presented in this dissertation will spur more interest in the integration of search and translation technologies

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

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 eectiveness 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 eectiveness 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

Foundation, Implementation and Evaluation of the MorphoSaurus System: Subword Indexing, Lexical Learning and Word Sense Disambiguation for Medical Cross-Language Information Retrieval

Im medizinischen Alltag, zu welchem viel Dokumentations- und Recherchearbeit gehört, ist mittlerweile der überwiegende Teil textuell kodierter Information elektronisch verfügbar. Hiermit kommt der Entwicklung leistungsfähiger Methoden zur effizienten Recherche eine vorrangige Bedeutung zu. Bewertet man die Nützlichkeit gängiger Textretrievalsysteme aus dem Blickwinkel der medizinischen Fachsprache, dann mangelt es ihnen an morphologischer Funktionalität (Flexion, Derivation und Komposition), lexikalisch-semantischer Funktionalität und der Fähigkeit zu einer sprachübergreifenden Analyse großer Dokumentenbestände. In der vorliegenden Promotionsschrift werden die theoretischen Grundlagen des MorphoSaurus-Systems (ein Akronym für Morphem-Thesaurus) behandelt. Dessen methodischer Kern stellt ein um Morpheme der medizinischen Fach- und Laiensprache gruppierter Thesaurus dar, dessen Einträge mittels semantischer Relationen sprachübergreifend verknüpft sind. Darauf aufbauend wird ein Verfahren vorgestellt, welches (komplexe) Wörter in Morpheme segmentiert, die durch sprachunabhängige, konzeptklassenartige Symbole ersetzt werden. Die resultierende Repräsentation ist die Basis für das sprachübergreifende, morphemorientierte Textretrieval. Neben der Kerntechnologie wird eine Methode zur automatischen Akquise von Lexikoneinträgen vorgestellt, wodurch bestehende Morphemlexika um weitere Sprachen ergänzt werden. Die Berücksichtigung sprachübergreifender Phänomene führt im Anschluss zu einem neuartigen Verfahren zur Auflösung von semantischen Ambiguitäten. Die Leistungsfähigkeit des morphemorientierten Textretrievals wird im Rahmen umfangreicher, standardisierter Evaluationen empirisch getestet und gängigen Herangehensweisen gegenübergestellt

Cross-language Information Retrieval

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for CLIR and outlines some open research questions.Comment: 49 pages, 0 figure

Mixed-Language Arabic- English Information Retrieval

Includes abstract.Includes bibliographical references.This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries

Zero-shot language transfer for cross-lingual sentence retrieval using bidirectional attention model

We present a neural architecture for cross-lingual mate sentence retrieval which encodes sentences in a joint multilingual space and learns to distinguish true translation pairs from semantically related sentences across languages. The proposed model combines a recurrent sequence encoder with a bidirectional attention layer and an intra-sentence attention mechanism. This way the final fixed-size sentence representations in each training sentence pair depend on the selection of contextualized token representations from the other sentence. The representations of both sentences are then combined using the bilinear product function to predict the relevance score. We show that, coupled with a shared multilingual word embedding space, the proposed model strongly outperforms unsupervised cross-lingual ranking functions, and that further boosts can be achieved by combining the two approaches. Most importantly, we demonstrate the model's effectiveness in zero-shot language transfer settings: our multilingual framework boosts cross-lingual sentence retrieval performance for unseen language pairs without any training examples. This enables robust cross-lingual sentence retrieval also for pairs of resource-lean languages, without any parallel data