A Study on Cross-Language Text and Image Retrieval
Date Issued
2005
Date
2005
Author(s)
Lin, Wen-Cheng
DOI
en-US
Abstract
Various types of digital data have an explosive growth nowadays. Retrieving the information we need effectively from so large amount of data is indispensable. In this dissertation, we investigate cross-language cross-media information retrieval, and consider two types of media, i.e. text and image. In cross-language cross-media information retrieval, language translation and media transformation are necessary to unify the representations of queries and documents.
First, we investigate cross-language information retrieval. Query translation is the main issue. Translation ambiguity, target polysemy and unknown words handling are dealt with. We use different linguistic resources to translate query. A Chinese-English WordNet and bilingual dictionary are used to deal with Chinese-English information retrieval. The best model achieves 0.1010 average precision, 69.23% of monolingual information retrieval. For named entity translation, a similarity-based backward transliteration framework is adopted. We propose an IR-based candidate filter to enhance the efficiency of the similarity-based backward transliteration.
We then investigate merging mechanisms in multilingual information retrieval. We consider two different MLIR architectures: centralized and distributed architectures. Several merging strategies are proposed. Normalized-by-top-k merging is proposed to normalize similarity scores. We also consider the retrieval effectiveness of each individual run in merging stage. Experimental results show that the proposed approaches are feasible in single and multiple IR system architectures. Normalized-by-top-k merging overcomes the drawback of normalized-score merging. Normalized-by-top-k merging with translation penalty could avoid performance drop down caused by a poor intermediate run.
We further extend the media to image and investigate cross-language image retrieval. We explore the integration of textual and visual information in image retrieval and propose a scheme to deal with cross-language image retrieval. An approach that automatically transforms textual queries into visual representations is proposed. Which query terms should be adopted to generate a visual query is investigated. Experimental results show that integrating textual and visual information improves retrieval performance. Nouns are appropriate to generate visual queries, while using named entities and verbs is helpless.
Subjects
跨語言檢索
查詢翻譯
結果彙整
彙整策略
跨語言圖片檢索
媒體轉換
自動產生影像查詢
cross-language cross-media information retrieval
cross-language information retrieval
query translation
multilingual information retrieval
result merging
merging strategy
ross-language image retrieval
media transformation
generated visual query
Type
thesis
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