Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

On the effectiveness of contextualisation techniques in spoken query spoken content retrieval

Racca, David and Jones, Gareth J.F. orcid logoORCID: 0000-0002-4033-9135 (2016) On the effectiveness of contextualisation techniques in spoken query spoken content retrieval. In: SIGIR ’16, 17-21 July 2016, Pisa, Italy. ISBN 978-1-4503-4069-4

Abstract
In passage and XML retrieval, contextualisation techniques seek to improve the rank of a relevant element by considering information from its surrounding elements and its container document. Recent research has demonstrated that some of these techniques are also particularly effective in spoken content retrieval tasks (SCR). However, no previous research has directly compared contextualisation techniques in an SCR setting, nor has it studied their potential to provide robustness to speech recognition errors. In this paper, we evaluate different contextualisation techniques, including a recently proposed technique based on positional language models (PLM) on the task of retrieving relevant spoken passages in response to a spoken query. We study the benefits of these techniques when queries and documents are transcribed with increasingly higher error rates. Experimental results over the Japanese NTCIR SpokenQuery&Doc collection show that combining global and local context is beneficial for SCR and that models usually benefit from using larger amounts of context in highly noisy conditions.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. . Association for Computing Machinery (ACM). ISBN 978-1-4503-4069-4
Publisher:Association for Computing Machinery (ACM)
Official URL:http://dx.doi.org/10.1145/2911451.2914730
Copyright Information:© 2016 ACM
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland through the CNGL Programme (Grant No: 12/CE/I2267) in the ADAPT Centre at Dublin City University.
ID Code:23393
Deposited On:31 May 2019 13:00 by Thomas Murtagh . Last Modified 06 Jan 2021 16:27
Documents

Full text available as:

[thumbnail of On_the_Effectiveness_of_Contextualisation_Techniques_in_Spoken_Query_Spoken_Content_Retrieval[1].pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
657kB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record