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2009 (38)
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Article (38)
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Hits 1 – 20 of 38
1
RMIT University at TREC 2009: Web Track
Garcia, Steven
In: DTIC (2009)
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2
A Novel Framework for Related Entities Finding: ICTNET at TREC 2009 Entity Track
Zhai, Haijun
;
Cheng, Xueqi
;
Guo, Jiafeng
...
In: DTIC (2009)
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3
Recognizing Connotative Meaning in Military Chat Communications
Budlong, Emily R.
;
Walter, Sharon M.
;
Yilmazel, Ozgur
In: DTIC (2009)
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4
Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues
Campbell, Gwendolyn E.
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Steinhauser, Natalie B.
;
Dzikovska, Myroslava
...
In: DTIC (2009)
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5
Relevance Feedback based on Constrained Clustering: FDU at TREC 09
Wang, Bingqing
;
Huang, Xuanjing
In: DTIC (2009)
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6
Translation Memory Technology Assessment
Van Ess-Dykema, Carol
;
Converse, Susan
;
White, John S
...
In: DTIC (2009)
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7
A Journey in Entity Related Retrieval for TREC 2009
Pamarthi, Jagadish
;
Zhou, Guangxu
;
Bayrak, Coskun
In: DTIC (2009)
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8
ICTNET at Web Track 2009 Diversity task
Bi, Wenjing
;
Yu, Xiaoming
;
Liu, Yue
...
In: DTIC (2009)
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9
Lucene for n-grams using the ClueWeb Collection
Newby, Gregory B.
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Fallen, Chris
;
McCormick, Kylie
In: DTIC (2009)
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10
BIT at TREC 2009 Faceted Blog Distillation Task
Jiang, Peng
;
Yang, Qing
;
Zhang, Chunxia
...
In: DTIC (2009)
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11
Recovering Asynchronous Watermark Tones from Speech
Morris, Robert
;
Johnson, Ralph
;
Goncharoff, Vladimir
...
In: DTIC (2009)
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12
Sparse Matrix Factorization: Applications to Latent Semantic Indexing
Moulding, Erin
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Kontostathis, April
;
Spiteri, Raymond J.
In: DTIC (2009)
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13
IRRA at TREC 2009: Index Term Weighting based on Divergence From Independence Model
Dincer, Bekir T.
;
Kocabase, Ilker
;
Karaoglan, Bahar
In: DTIC (2009)
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14
Semantic Search
Martell, Craig
In: DTIC (2009)
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15
Delft University at the TREC 2009 Entity Track: Ranking Wikipedia Entities
Serdyukov, Pavel
;
de Vries, Arjen
In: DTIC (2009)
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16
POSTECH at TREC 2009 Blog Track: Top Stories Identification
Lee, Yeha
;
Jung, Hun-young
;
Song, Woosang
...
In: DTIC (2009)
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17
Entity Retrieval by Hierarchical Relevance Model, Exploiting the Structure of Tables and Learning Homepage Classifiers
Fang, Yi
;
Si, Luo
;
Yu, Zhengtao
...
In: DTIC (2009)
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18
University of Padua at TREC 2009: Relevance Feedback Track
Di Buccio, Emanuele
;
Melucci, Massimo
In: DTIC (2009)
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19
Related Entity Finding Based on Co-Occurrence
Bron, Marc
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Balog, Krisztian
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Rijke, Maarten de
In: DTIC (2009)
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20
Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia
Kaptein, Rianne
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Koolen, Marijn
;
Kamps, Jaap
In: DTIC (2009)
Abstract:
In this paper, we document our efforts in participating to the TREC 2009 Entity Ranking and Web Tracks. We had multiple aims: For the Web Track's Adhoc task we experiment with document text and anchor text representation, and the use of the link structure. For the Web Track's Diversity task we experiment with using a top down sliding window that, given the top ranked documents, chooses as the next ranked document the one that has the most unique terms or links. We test our sliding-window method on a standard document text index and an index of propagated anchor texts. We also experiment with extreme query expansions by taking the top n results of the initial ranking as multi-faceted aspects of the topic to construct n relevance models to obtain n sets of results. A final diverse set of results is obtained by merging the n results lists. For the Entity Ranking Track, we also explore the effectiveness of the anchor text representation, look at the co-citation graph, and experiment with using Wikipedia as a pivot. Our main findings can be summarized as follows: Anchor text is very effective for diversity. It gives high early precision and the results cover more relevant sub-topics than the document text index. Our baseline runs have low diversity, which limits the possible impact of the sliding window approach. New link information seems more effective for diversifying text-based search results than the amount of unique terms added by a document. In the entity ranking task, anchor text finds few primary pages , but it does retrieve a large number of relevant pages. Using Wikipedia as a pivot results in large gains of P10 and NDCG when only primary pages are considered. Although the links between the Wikipedia entities and pages in the Clueweb collection are sparse, the precision of the existing links is very high. ; Presented at the Text REtrieval Conference (TREC 2009, 18th) held in Gaithersburg, Maryland on 17-20 November 2009. Published in the Proceedings of the Text REtrieval Conference (TREC 2009, 18th), 2009. The conference was co-sponsored by the National Institute of Standards and Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA).
Keyword:
*CLUEWEB
;
*INFORMATION RETRIEVAL
;
*INTERNET
;
*NDCG(NORMALIZED DISCOUNTED CUMULATIVE GAIN)
;
*SEMANTICS
;
*TEXT PROCESSING
;
*WEB TRACK COMPUTER SYSTEM
;
ANCHOR TEXT
;
BASE LINES
;
CATEGORY MAPPING
;
Computer Programming and Software
;
Equipment and Methods
;
FOREIGN REPORTS
;
INFORMATION PROCESSING
;
Information Science
;
INTERNET BROWSERS
;
Linguistics
;
LINK FILTERS
;
NETHERLANDS
;
PIVOTS
;
REPRINTS
;
SLIDING
;
SLIDING WINDOWS
;
SOCIAL COMMUNICATION
;
SYMPOSIA
;
TERM FILTERS
;
Test Facilities
;
TEXTBOOKS
;
WIKIPEDIA
;
WINDOWS
URL:
http://www.dtic.mil/docs/citations/ADA517853
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517853
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