1,665 research outputs found

    An experiment in audio classification from compressed data

    Get PDF
    In this paper we present an algorithm for automatic classification of sound into speech, instrumental sound/ music and silence. The method is based on thresholding of features derived from the modulation envelope of the frequency limited audio signal. Four characteristics are examined for discrimination: the occurrence and duration of energy peaks, rhythmic content and the level of harmonic content. The proposed algorithm allows classification directly on MPEG-1 audio bitstreams. The performance of the classifier was evaluated on TRECVID test data. The test results are above-average among all TREC participants. The approaches adopted by other research groups participating in TREC are also discussed

    DCU@TRECMed 2012: Using ad-hoc baselines for domain-specific retrieval

    Get PDF
    This paper describes the first participation of DCU in the TREC Medical Records Track (TRECMed). We performed some initial experiments on the 2011 TRECMed data based on the BM25 retrieval model. Surprisingly, we found that the standard BM25 model with default parameters, performs comparable to the best automatic runs submitted to TRECMed 2011 and would have resulted in rank four out of 29 participating groups. We expected that some form of domain adaptation would increase performance. However, results on the 2011 data proved otherwise: concept-based query expansion decreased performance, and filtering and reranking by term proximity also decreased performance slightly. We submitted four runs based on the BM25 retrieval model to TRECMed 2012 using standard BM25, standard query expansion, result filtering, and concept-based query expansion. Official results for 2012 confirm that domain-specific knowledge does not increase performance compared to the BM25 baseline as applied by us

    Ontology-Based MEDLINE Document Classification

    Get PDF
    An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents

    The TREC2001 video track: information retrieval on digital video information

    Get PDF
    The development of techniques to support content-based access to archives of digital video information has recently started to receive much attention from the research community. During 2001, the annual TREC activity, which has been benchmarking the performance of information retrieval techniques on a range of media for 10 years, included a ”track“ or activity which allowed investigation into approaches to support searching through a video library. This paper is not intended to provide a comprehensive picture of the different approaches taken by the TREC2001 video track participants but instead we give an overview of the TREC video search task and a thumbnail sketch of the approaches taken by different groups. The reason for writing this paper is to highlight the message from the TREC video track that there are now a variety of approaches available for searching and browsing through digital video archives, that these approaches do work, are scalable to larger archives and can yield useful retrieval performance for users. This has important implications in making digital libraries of video information attainable

    Web Page Retrieval by Combining Evidence

    Get PDF
    The participation of the REINA Research Group in WebCLEF 2005 focused in the monolingual mixed task. Queries or topics are of two types: named and home pages. For both, we first perform a search by thematic contents; for the same query, we do a search in several elements of information from every page (title, some meta tags, anchor text) and then we combine the results. For queries about home pages, we try to detect using a method based in some keywords and their patterns of use. After, a re-rank of the results of the thematic contents retrieval is performed, based on Page-Rank and Centrality coeficients

    The troubles with using a logical model of IR on a large collection of documents

    Get PDF
    This is a paper of two halves. First, a description of a logical model of IR known as imaging will be presented. Unfortunately due to constraints of time and computing resource this model was not implemented in time for this round of TREC. Therefore this paper's second half describes the more conventional IR model and system used to generate the Glasgow IR result set (glair1)

    The relationship of word error rate to document ranking

    Get PDF
    This paper describes two experiments that examine the relationship of Word Error Rate (WER) of retrieved spoken documents returned by a spoken document retrieval system. Previous work has demonstrated that recognition errors do not significantly affect retrieval effectiveness but whether they will adversely affect relevance judgement remains unclear. A user-based experiment measuring ability to judge relevance from the recognised text presented in a retrieved result list was conducted. The results indicated that users were capable of judging relevance accurately despite transcription errors. This lead an examination of the relationship of WER in retrieved audio documents to their rank position when retrieved for a particular query. Here it was shown that WER was somewhat lower for top ranked documents than it was for documents retrieved further down the ranking, thereby indicating a possible explanation for the success of the user experiment

    Investigating cross-language speech retrieval for a spontaneous conversational speech collection

    Get PDF
    Cross-language retrieval of spontaneous speech combines the challenges of working with noisy automated transcription and language translation. The CLEF 2005 Cross-Language Speech Retrieval (CL-SR) task provides a standard test collection to investigate these challenges. We show that we can improve retrieval performance: by careful selection of the term weighting scheme; by decomposing automated transcripts into phonetic substrings to help ameliorate transcription errors; and by combining automatic transcriptions with manually-assigned metadata. We further show that topic translation with online machine translation resources yields effective CL-SR
    corecore