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hypothesised sources of recall loss in
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. While NE type constraints substantially
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work is closest to Temporal KBP
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systems . The CUNY and UNED systems
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Chen et al. , 2010 ) . We used a
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toolkit ( Chen et al. , 2011
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8088 " in Fig_10 ) . Control of
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is also supported by an inferential
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summarization . Together with
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procedures they build up the
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. 8 Conclusion Recent TAC KBP
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results have shown that state-of-the-art
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already occupied . Therefore no
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at all is performed . frame-i
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the head caseframe before the
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takes place . These transformations
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tion , they defined the task as a
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problem for each of the opinions
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demonstrated by recent performance in TAC
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. We isolate this recall loss
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filters on the TAC KBP English
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2011 corpus , queries and task
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systematically analysing each stage of the
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pipeline as a filter over correct
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Traditional approaches do matching ( for
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) on all sentences , even though
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permitted entry the appropriate
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is performed ~ thus acquiring
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Analysing recall loss in named entity
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</title> Glen Pink Joel Nothman
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perform a maximum recall analysis of
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, providing a comprehensive analysis
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patterns of property assignment (
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) are continuously evolving in
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and event extraction , template
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, and author name deduplication
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% of recall is ignored by most
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systems due to NER error , and
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slots that are extracted by the
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toolkit ( Chen et al. , 2011
|