1,230 research outputs found
Overview of the CLEF-2005 cross-language speech retrieval track
The task for the CLEF-2005 cross-language speech retrieval track was to identify topically coherent segments of English interviews in a known-boundary condition. Seven teams participated, performing both monolingual and cross-language searches of ASR transcripts, automatically generated metadata, and manually generated metadata.
Results indicate that monolingual search technology is sufficiently accurate to be useful for some purposes (the
best mean average precision was 0.18) and cross-language searching yielded results typical of those seen in other
applications (with the best systems approximating monolingual mean average precision)
Investigating cross-language speech retrieval for a spontaneous conversational speech collection
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
Examining the contributions of automatic speech transcriptions and metadata sources for searching spontaneous conversational speech
The searching spontaneous speech can be enhanced by combining automatic speech transcriptions with semantically
related metadata. An important question is what can be expected from search of such transcriptions and different
sources of related metadata in terms of retrieval effectiveness. The Cross-Language Speech Retrieval (CL-SR) track at recent CLEF workshops provides a spontaneous speech
test collection with manual and automatically derived metadata fields. Using this collection we investigate the comparative search effectiveness of individual fields comprising automated transcriptions and the available metadata. A further important question is how transcriptions and metadata should be combined for the greatest benefit to search accuracy. We compare simple field merging of individual fields with the extended BM25 model for weighted field combination (BM25F). Results indicate that BM25F can produce improved search accuracy, but that it is currently important to set its parameters suitably using a suitable training set
An evaluation resource for geographic information retrieval
In this paper we present an evaluation resource for geographic information retrieval developed within the Cross Language Evaluation
Forum (CLEF). The GeoCLEF track is dedicated to the evaluation of geographic information retrieval systems. The resource
encompasses more than 600,000 documents, 75 topics so far, and more than 100,000 relevance judgments for these topics. Geographic
information retrieval requires an evaluation resource which represents realistic information needs and which is geographically
challenging. Some experimental results and analysis are reported
Dublin City University at CLEF 2007: Cross-Language Speech Retrieval Experiments
The Dublin City University participation in the CLEF 2007 CL-SR English task concentrated primarily on issues of topic translation. Our retrieval system used the BM25F model and pseudo relevance feedback. Topics were translated into English using the Yahoo! BabelFish free online service combined with domain-specific translation lexicons gathered automatically from Wikipedia. We explored alternative topic translation methods using these resources. Our results indicate that extending machine translation tools using automatically generated domainspecific translation lexicons can provide improved CLIR effectiveness for this task
GeoCLEF 2007: the CLEF 2007 cross-language geographic information retrieval track overview
GeoCLEF ran as a regular track for the second time within the Cross
Language Evaluation Forum (CLEF) 2007. The purpose of GeoCLEF is to test
and evaluate cross-language geographic information retrieval (GIR): retrieval
for topics with a geographic specification. GeoCLEF 2007 consisted of two sub
tasks. A search task ran for the third time and a query classification task was
organized for the first. For the GeoCLEF 2007 search task, twenty-five search
topics were defined by the organizing groups for searching English, German,
Portuguese and Spanish document collections. All topics were translated into
English, Indonesian, Portuguese, Spanish and German. Several topics in 2007
were geographically challenging. Thirteen groups submitted 108 runs. The
groups used a variety of approaches. For the classification task, a query log
from a search engine was provided and the groups needed to identify the
queries with a geographic scope and the geographic components within the
local queries
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
Multilingual interactive experiments with Flickr
This paper presents a proposal for iCLEF 2006, the interactive track
of the CLEF cross-language evaluation campaign. In the past, iCLEF has
addressed applications such as information retrieval and question answering. However, for 2006 the focus
has turned to text-based image retrieval from Flickr. We describe
Flickr, the challenges this kind of collection presents to
cross-language researchers, and suggest initial iCLEF tasks
Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment
VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided.
The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the âBeeldenstormâ collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called âFinding Related Resources Across Languages,â involved linking video to material on the same subject in a different language.
Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language âBeeldenstormâ collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the
speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names
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