Journal Article


Gareth J.F. Jones
Debasis Ganguly
Ahmad Khwileh


Computer Science

machine translating information retrieval automatic speech recognition asr multimedia systems information storage and retrieval systems cross language information retrieval internet video speech retrieval

Utilisation of metadata fields and query expansion in cross-lingual search of user-generated Internet video (2016)

Abstract 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.
Collections Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Publication Type = Article
Ireland -> Dublin City University -> Subject = Computer Science: Information storage and retrieval systems
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Centre for Next Generation Localisation (CNGL)
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating
Ireland -> Dublin City University -> Subject = Computer Science: Multimedia systems
Ireland -> Dublin City University -> Subject = Computer Science: Information retrieval

Full list of authors on original publication

Gareth J.F. Jones, Debasis Ganguly, Ahmad Khwileh

Experts in our system

Gareth J. F. Jones
Dublin City University
Total Publications: 297
Debasis Ganguly
Dublin City University
Total Publications: 40
Ahmad Khwileh
Dublin City University