Searching and organizing images across languages

With the continual growth of users on the Web from a wide range of countries, supporting such users in their search of cultural heritage collections will grow in importance. In the next few years, the growth areas of Internet users will come from the Indian sub-continent and China. Consequently, if holders of cultural heritage collections wish their content to be viewable by the full range of users coming to the Internet, the range of languages that they need to support will have to grow. This paper will present recent work conducted at the University of Sheffield (and now being implemented in BRICKS) on how to use automatic translation to provide search and organisation facilities for a historical image search engine. The system allows users to search for images in seven different languages, providing means for the user to examine translated image captions and browse retrieved images organised by categories written in their native language

User experiments with the Eurovision cross-language image retrieval system

In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval. The system is evaluated by multilingual users for two search tasks with the system configured in English and five other languages. To our knowledge this is the first published set of user experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual search engine using little knowledge of any language other than English, (2) categorizing images assists the user's search, and (3) there are differences in the way users search between the proposed search tasks. Based on the two search tasks and user feedback, we describe important aspects of any CL image retrieval system

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

Arabic Information Retrieval: A Relevancy Assessment Survey

The paper presents a research in Arabic Information Retrieval (IR). It surveys the impact of statistical and morphological analysis of Arabic text in improving Arabic IR relevancy. We investigated the contributions of Stemming, Indexing, Query Expansion, Text Summarization (TS), Text Translation, and Named Entity Recognition (NER) in enhancing the relevancy of Arabic IR. Our survey emphasizing on the quantitative relevancy measurements provided in the surveyed publications. The paper shows that the researchers achieved significant enhancements especially in building accurate stemmers, with accuracy reaches 97%, and in measuring the impact of different indexing strategies. Query expansion and Text Translation showed positive relevancy effect. However, other tasks such as NER and TS still need more research to realize their impact on Arabic IR

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

Utilisation of metadata fields and query expansion in cross-lingual search of user-generated Internet video

Recent years have seen signicant eorts in the area of Cross Language Information Retrieval (CLIR) for text retrieval. This work initially focused on formally published content, but more recently research has begun to concentrate on CLIR for informal social media content. However, despite the current expansion in online multimedia archives, there has been little work on CLIR for this content. While there has been some limited work on Cross-Language Video Retrieval (CLVR) for professional videos, such as documentaries or TV news broadcasts, there has to date, been no signicant investigation of CLVR for the rapidly growing archives of informal user generated (UGC) content. Key differences between such UGC and professionally produced content are the nature and structure of the textual UGC metadata associated with it, as well as the form and quality of the content itself. In this setting, retrieval eectiveness may not only suer from translation errors common to all CLIR tasks, but also recognition errors associated with the automatic speech recognition (ASR) systems used to transcribe the spoken content of the video and with the informality and inconsistency of the associated user-created metadata for each video. This work proposes and evaluates techniques to improve CLIR effectiveness of such noisy UGC content. Our experimental investigation shows that dierent sources of evidence, e.g. the content from dierent elds of the structured metadata, significantly affect CLIR effectiveness. Results from our experiments also show that each metadata eld has a varying robustness to query expansion (QE) and hence can have a negative impact on the CLIR eectiveness. Our work proposes a novel adaptive QE technique that predicts the most reliable source for expansion and shows how this technique can be effective for improving CLIR effectiveness for UGC content

Improving Retrieval Results with discipline-specific Query Expansion

Choosing the right terms to describe an information need is becoming more difficult as the amount of available information increases. Search-Term-Recommendation (STR) systems can help to overcome these problems. This paper evaluates the benefits that may be gained from the use of STRs in Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and one giving general recommendations, and compare the retrieval performance of these STRs. The main findings are: (1) QE with specific STRs leads to significantly better results than QE with a general STR, (2) QE with specific STRs selected by a heuristic mechanism of topic classification leads to better results than the general STR, however (3) selecting the best matching specific STR in an automatic way is a major challenge of this process.Comment: 6 pages; to be published in Proceedings of Theory and Practice of Digital Libraries 2012 (TPDL 2012

DCU and UTA at ImageCLEFPhoto 2007

Dublin City University (DCU) and University of Tampere(UTA) participated in the ImageCLEF 2007 photographic ad-hoc retrieval task with several monolingual and bilingual runs. Our approach was language independent: text retrieval based on fuzzy s-gram query translation was combined with visual retrieval. Data fusion between text and image content was performed using unsupervised query-time weight generation approaches. Our baseline was a combination of dictionary-based query translation and visual retrieval, which achieved the best result. The best mixed modality runs using fuzzy s-gram translation achieved on average around 83% of the performance of the baseline. Performance was more similar when only top rank precision levels of P10 and P20 were considered. This suggests that fuzzy sgram query translation combined with visual retrieval is a cheap alternative for cross-lingual image retrieval where only a small number of relevant items are required. Both sets of results emphasize the merit of our query-time weight generation schemes for data fusion, with the fused runs exhibiting marked performance increases over single modalities, this is achieved without the use of any prior training data