Domain-specific query translation for multilingual information access using machine translation augmented with dictionaries mined from Wikipedia

Accurate high-coverage translation is a vital component of reliable cross language information access (CLIA) systems. While machine translation (MT) has been shown to be effective for CLIA tasks in previous evaluation workshops, it is not well suited to specialized tasks where domain specific translations are required. We demonstrate that effective query translation for CLIA can be achieved in the domain of cultural heritage (CH). This is performed by augmenting a standard MT system with domainspecific phrase dictionaries automatically mined from the online Wikipedia. Experiments using our hybrid translation system with sample query logs from users of CH websites demonstrate a large improvement in the accuracy of domain specific phrase detection and translation

When to cross Over? Cross-language linking using Wikipedia for VideoCLEF 2009

We describe Dublin City University (DCU)'s participation in the VideoCLEF 2009 Linking Task. Two approaches were implemented using the Lemur information retrieval toolkit. Both approaches rst extracted a search query from the transcriptions of the Dutch TV broadcasts. One method rst performed search on a Dutch Wikipedia archive, then followed links to corresponding pages in the English Wikipedia. The other method rst translated the extracted query using machine translation and then searched the English Wikipedia collection directly. We found that using the original Dutch transcription query for searching the Dutch Wikipedia yielded better results

Applying digital content management to support localisation

The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM

Hybrid and Interactive Domain-Specific Translation for Multilingual Access to Digital Libraries

Accurate high-coverage translation is a vital component of reliable cross language information retrieval (CLIR) systems. This is particularly true for retrieval from archives such as Digital Libraries which are often specific to certain domains. While general machine translation (MT) has been shown to be effective for CLIR tasks in laboratory information retrieval evaluation tasks, it is generally not well suited to specialized situations where domain-specific translations are required. We demonstrate that effective query translation in the domain of cultural heritage (CH) can be achieved using a hybrid translation method which augments a standard MT system with domain-specific phrase dictionaries automatically mined from Wikipedia. We further describe the use of these components in a domain-specific interactive query translation service. The interactive system selects the hybrid translation by default, with other possible translations being offered to the user interactively to enable them to select alternative or additional translation(s). The objective of this interactive service is to provide user control of translation while maximising translation accuracy and minimizing the translation effort of the user. Experiments using our hybrid translation system with sample query logs from users of CH websites demonstrate a large improvement in the accuracy of domain-specific phrase detection and translation

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

Mining Meaning from Wikipedia

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.Comment: An extensive survey of re-using information in Wikipedia in natural language processing, information retrieval and extraction and ontology building. Accepted for publication in International Journal of Human-Computer Studie

Analysis of errors in the automatic translation of questions for translingual QA systems

Purpose – This study aims to focus on the evaluation of systems for the automatic translation of questions destined to translingual question-answer (QA) systems. The efficacy of online translators when performing as tools in QA systems is analysed using a collection of documents in the Spanish language. Design/methodology/approach – Automatic translation is evaluated in terms of the functionality of actual translations produced by three online translators (Google Translator, Promt Translator, and Worldlingo) by means of objective and subjective evaluation measures, and the typology of errors produced was identified. For this purpose, a comparative study of the quality of the translation of factual questions of the CLEF collection of queries was carried out, from German and French to Spanish. Findings – It was observed that the rates of error for the three systems evaluated here are greater in the translations pertaining to the language pair German-Spanish. Promt was identified as the most reliable translator of the three (on average) for the two linguistic combinations evaluated. However, for the Spanish-German pair, a good assessment of the Google online translator was obtained as well. Most errors (46.38 percent) tended to be of a lexical nature, followed by those due to a poor translation of the interrogative particle of the query (31.16 percent). Originality/value – The evaluation methodology applied focuses above all on the finality of the translation. That is, does the resulting question serve as effective input into a translingual QA system? Thus, instead of searching for “perfection”, the functionality of the question and its capacity to lead one to an adequate response are appraised. The results obtained contribute to the development of improved translingual QA systems

Analysis of errors in the automatic translation of questions for translingual QA systems

Purpose – This study aims to focus on the evaluation of systems for the automatic translation of questions destined to translingual question-answer (QA) systems. The efficacy of online translators when performing as tools in QA systems is analysed using a collection of documents in the Spanish language. Design/methodology/approach – Automatic translation is evaluated in terms of the functionality of actual translations produced by three online translators (Google Translator, Promt Translator, and Worldlingo) by means of objective and subjective evaluation measures, and the typology of errors produced was identified. For this purpose, a comparative study of the quality of the translation of factual questions of the CLEF collection of queries was carried out, from German and French to Spanish. Findings – It was observed that the rates of error for the three systems evaluated here are greater in the translations pertaining to the language pair German-Spanish. Promt was identified as the most reliable translator of the three (on average) for the two linguistic combinations evaluated. However, for the Spanish-German pair, a good assessment of the Google online translator was obtained as well. Most errors (46.38 percent) tended to be of a lexical nature, followed by those due to a poor translation of the interrogative particle of the query (31.16 percent). Originality/value – The evaluation methodology applied focuses above all on the finality of the translation. That is, does the resulting question serve as effective input into a translingual QA system? Thus, instead of searching for “perfection”, the functionality of the question and its capacity to lead one to an adequate response are appraised. The results obtained contribute to the development of improved translingual QA systems