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General Terms
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In: http://www.clef-campaign.org/2007/working_notes/kulaclef2007.pdf
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| 322 |
Combining Probabilistic and Translation-Based Models for Information Retrieval based on Word Sense Annotations
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In: http://elara.tk.informatik.tu-darmstadt.de/publications/2009/wolf-paperCLEF2009.pdf
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Cheshire at GeoCLEF 2007: Retesting Text Retrieval Baselines
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In: http://www.clef-campaign.org/2007/working_notes/larsonclef2007geo.pdf
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Cross-lingual Information Retrieval with Explicit Semantic Analysis
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In: http://www.aifb.uni-karlsruhe.de/WBS/pso/publications/sorg_paperCLEF2008.pdf
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| 325 |
University of Hagen at GeoCLEF 2008: Combining IR and QA for Geographic Information Retrieval
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In: http://www.clef-campaign.org/2008/working_notes/leveling-paperCLEF2008.pdf
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DCU and UTA at ImageCLEFPhoto 2007
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In: http://www.clef-campaign.org/2007/working_notes/JarvelinCLEF2007.pdf
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| 327 |
OPTIMISING THE EMAIL KNOWLEDGE EXTRACTION SYSTEM TO SUPPORT KNOWLEDGE WORK
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In: http://is2.lse.ac.uk/asp/aspecis/20070167.pdf
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Abstract:
Although employees ’ expertise has for some time been seen as a vital knowledge asset in organisations, it is only lately that it started to attract researchers ’ attention. As a result, interest in automated systems that aim at enhancing the visibility and traceability of employees with particular expertise is growing. This research focuses on one critical everyday organisational business tool-email, as an information source to help locate employees with particular expertise within the organisation. This paper presents the process for keyphrase extraction from email messages. The process uses machine learning to tag new text by its part of speech, then extracts keyphrases purely based on part-of-speech (POS) tags that surround these phrases. The system has been evaluated using three datasets. Results show that the use of the linguistic tool, WordNet, improves to some extent the precision, recall, and f-measure metrics. The goal of this work is to advance our understanding of what may (or may not) be effective in extracting information from email to help identify experts.
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Keyword:
Email; Expertise identification; Keyphrase extraction; Knowledge management; Performance Measurement. 681
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URL: http://is2.lse.ac.uk/asp/aspecis/20070167.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.8590
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| 328 |
Using IR-n for Information retrieval of Genomics Track
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In: http://trec.nist.gov/pubs/trec16/papers/ualicante.geo.final.pdf
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| 329 |
Prompt and Rater Effects in Second Language Writing Performance Assessment.
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