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Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia
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In: DTIC (2009)
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82 |
PRIS at 2009 Relevance Feedback track: Experiments in Language Model for Relevance Feedback
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In: DTIC (2009)
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83 |
A Study of Faceted Blog Distillation -- PRIS at TREC 2009 Blog Track
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In: DTIC (2009)
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85 |
Formulating Simple Structured Queries using Temporal and Distributional Cues in Patents
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In: DTIC (2009)
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86 |
Experiments on Related Entity Finding Track at TREC 2009
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In: DTIC (2009)
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87 |
THUIR at TREC 2009 Web Track: Finding Relevant and Diverse Results for Large Scale Web Search
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In: DTIC (2009)
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88 |
PARADISE Based Search Engine at TREC 2009 Web Track
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In: DTIC (2009)
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89 |
Microsoft Research at TREC 2009. Web and Relevance Feedback Tracks
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In: DTIC (2009)
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90 |
FEUP at TREC 2009 Blog Track: Temporal Evidence in the Faceted Blog Distillation Task
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In: DTIC (2009)
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91 |
A Comparison of Query-by-Example Methods for Spoken Term Detection
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In: DTIC (2009)
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92 |
The Multi-Session Audio Research Project (MARP) Corpus: Goals, Design and Initial Findings
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In: DTIC (2009)
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93 |
Long Term Examination of Intra-Session and Inter-Session Speaker Variability
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In: DTIC (2009)
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95 |
Perturbation and Pitch Normalization as Enhancements to Speaker Recognition
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In: DTIC (2009)
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96 |
Long Term Examination of Intra-Session and Inter-Session Speaker Variability
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In: DTIC (2009)
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97 |
Finding Related Entities by Retrieving Relations: UIUC at TREC 2009 Entity Track
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In: DTIC (2009)
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Abstract:
Our goal in participating in the TREC 2009 Entity Track was to study whether relation extraction techniques can help in improving accuracy of the entity finding task. Finding related entities is informational in nature and we wanted to explore if inducing structure on the queries helps satisfy this information need. The research outlook we took was to study techniques that retrieve relations between two entities from a large corpus, and from those, find the most relevant entities that participate in the given relation with another given entity. Instead of aiming at retrieving pages about specific entities, we tried to address the problem of directly finding the entities from the text. Our experimental results show that we were able to find many related entities using relation-based extraction, and ranking entities based on further evidence from the text helps to a certain extent. ; Presented at the Text REtrieval Conference (TREC 2009) (18th) held in Gaithersburg, Maryland, November 17-20, 2009. Published in 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). The original document contains color images.
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Keyword:
*INFORMATION RETRIEVAL; ACCURACY; ENTITIES; EXTRACTION; FORMULATIONS; Information Science; SYMPOSIA; SYSTEMS ENGINEERING
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517759 http://www.dtic.mil/docs/citations/ADA517759
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98 |
Facet Classification of Blogs: Know-Center at the TREC 2009 Blog Distillation Task
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In: DTIC (2009)
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99 |
Patent Retrieval in Chemistry based on Semantically Tagged Named Entities
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In: DTIC (2009)
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100 |
A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)
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In: DTIC (2008)
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