1,681 research outputs found

    Multilingual adaptive search for digital libraries

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    This paper describes a framework for Adaptive Multilingual Information Retrieval (AMIR) which allows multilingual resource discovery and delivery using on-the-fly machine translation of documents and queries. Result documents are presented to the user in a contextualised manner. Challenges and affordances of both Adaptive and Multilingual IR, with a particular focus on Digital Libraries, are detailed. The framework components are motivated by a series of results from experiments on query logs and documents from The European Library. We conclude that factoring adaptivity and multilinguality aspects into the search process can enhance the user’s experience with online Digital Libraries

    DCU-TCD@LogCLEF 2010: re-ranking document collections and query performance estimation

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    This paper describes the collaborative participation of Dublin City University and Trinity College Dublin in LogCLEF 2010. Two sets of experiments were conducted. First, different aspects of the TEL query logs were analysed after extracting user sessions of consecutive queries on a topic. The relation between the queries and their length (number of terms) and position (first query or further reformulations) was examined in a session with respect to query performance estimators such as query scope, IDF-based measures, simplified query clarity score, and average inverse document collection frequency. Results of this analysis suggest that only some estimator values show a correlation with query length or position in the TEL logs (e.g. similarity score between collection and query). Second, the relation between three attributes was investigated: the user's country (detected from IP address), the query language, and the interface language. The investigation aimed to explore the influence of the three attributes on the user's collection selection. Moreover, the investigation involved assigning different weights to the three attributes in a scoring function that was used to re-rank the collections displayed to the user according to the language and country. The results of the collection re-ranking show a significant improvement in Mean Average Precision (MAP) over the original collection ranking of TEL. The results also indicate that the query language and interface language have more in uence than the user's country on the collections selected by the users

    Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

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    It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201

    Multilingual Information Access: Practices and Perceptions of Bi/multilingual Academic Users

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    The research reported in this dissertation explored linguistic determinants in online information searching, and examined to what extent bi/multilingual academic users utilize Multilingual Information Access (MLIA) tools and what impact these have on their information searching behavior. The aim of the study was three-pronged: to provide tangible data that can support recommendations for the effective user-centered design of Multilingual Information Retrieval (MLIR) systems; to provide a user-centered evaluation of existing MLIA tools, and to offer the basis of a framework for Library & Information Science (LIS) professionals in teaching information literacy and library skills for bi/multilingual academic users. In the first phase of the study, 250 bi/multilingual students participated in a web survey that investigated their language choices while searching for information on the internet and electronic databases. 31 of these participants took part in the second phase which involved a controlled lab-based user experiment and post experiment questionnaire that investigated their use of MLIA tools on Google and WorldCat and their opinions of these tools. In the third phase, 19 students participated in focus groups discussions and 6 librarians were interviewed to find out their perspectives on multilingual information literacy. Results showed that though machine translation has alleviated some of the linguistic related challenges in online information searching, language barriers do still exist for some users especially at the query formulation stage. Captures from the experiment revealed great diversity in the way MLIA tools were utilized while the focus group discussions and interviews revealed a general lack of awareness by both librarians and students of the tools that could help enhance and promote multilingual information literacy. The study highlights the roles of both IR system designers as well as LIS professionals in enhancing and promoting multilingual information access and literacy: User- centered design, user-modeling were found to be key aspects in the development of more effective multilingual information retrieval (MLIR) systems. The study also highlights the distinction between being multilingually information literate and being multilingual information literate. Suitable models for instruction for bi/multilingual academic users point towards Specialized Information Literacy Instruction (SILI) and Personalized Information Literacy Instruction (PILI)
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