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  • Association for Computing Machinery (ACM)  (2)
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  • Association for Computing Machinery (ACM)  (2)
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  • 1
    Online Resource
    Online Resource
    YASS : Yet another suffix stripper
    Association for Computing Machinery (ACM) ; 2007
    In:  ACM Transactions on Information Systems Vol. 25, No. 4 ( 2007-10), p. 18-
    Details
    In: ACM Transactions on Information Systems, Association for Computing Machinery (ACM), Vol. 25, No. 4 ( 2007-10), p. 18-
    Abstract: Stemmers attempt to reduce a word to its stem or root form and are used widely in information retrieval tasks to increase the recall rate. Most popular stemmers encode a large number of language-specific rules built over a length of time. Such stemmers with comprehensive rules are available only for a few languages. In the absence of extensive linguistic resources for certain languages, statistical language processing tools have been successfully used to improve the performance of IR systems. In this article, we describe a clustering-based approach to discover equivalence classes of root words and their morphological variants. A set of string distance measures are defined, and the lexicon for a given text collection is clustered using the distance measures to identify these equivalence classes. The proposed approach is compared with Porter's and Lovin's stemmers on the AP and WSJ subcollections of the Tipster dataset using 200 queries. Its performance is comparable to that of Porter's and Lovin's stemmers, both in terms of average precision and the total number of relevant documents retrieved. The proposed stemming algorithm also provides consistent improvements in retrieval performance for French and Bengali, which are currently resource-poor.
    Type of Medium: Online Resource
    ISSN: 1046-8188 , 1558-2868
    URL: Article
    DOI: 10.1145/1281485.1281489
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2007
    detail.hit.zdb_id: 602352-6
    detail.hit.zdb_id: 2006337-4
    SSG: 24,1
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  • 2
    Online Resource
    Online Resource
    Query-by-Example Spoken Term Detection for Zero-Resource Languages Using Heuristic Search
    Association for Computing Machinery (ACM) ; 2023
    In:  ACM Transactions on Asian and Low-Resource Language Information Processing
    Details
    In: ACM Transactions on Asian and Low-Resource Language Information Processing, Association for Computing Machinery (ACM)
    Abstract: Query-by-Example spoken content retrieval is a demanding and challenging task when a large volume of spoken content is piled up in the repositories without annotation. In the absence of annotation, spoken content retrieval is achieved by capturing the similarities between the query and spoken terms from the acoustic feature representation itself. Dynamic Time Warping (DTW) centric techniques identify the optimal alignment between the acoustic feature representations and capture the similarities between query and spoken terms. Despite feasibility, the DTW-centric techniques produce a lot of false alarms due to the variabilities that exist in natural speech and degrade the performance. In the proposed approach, the variability challenges are addressed in two stages. At first, the speaker-independent acoustic feature representation was obtained from the deep convolutional neural networks that reduce the speaker variabilities. In the second stage, the similarities between the query and spoken term were captured using the heuristic search method. The proposed approach was compared with other state-of-the-art methods using Microsoft Low-Resource Language speech corpus. A 3% improvement and 32% reduction in the hit and false alarm ratio were achieved across languages.
    Type of Medium: Online Resource
    ISSN: 2375-4699 , 2375-4702
    URL: Article
    DOI: 10.1145/3609505
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2023
    detail.hit.zdb_id: 2820615-0
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