Concordance

Query H05-1115 20 (640.3 per million)

tech,5-1-H05-1115,ak	We consider the problem of <term> question-focused sentence retrieval </term> from complex news articles describing multi-event stories published over time .	#5754 We consider the problem ofquestion-focused sentence retrieval from complex news articles describing multi-event stories published over time.
other,0-2-H05-1115,ak	We consider the problem of <term> question-focused sentence retrieval </term> from complex news articles describing multi-event stories published over time . <term> Annotators </term> generated a list of <term> questions </term> central to understanding each story in our <term> corpus </term> .	#5768 We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time.Annotators generated a list of questions central to understanding each story in our corpus.
other,5-2-H05-1115,ak	<term> Annotators </term> generated a list of <term> questions </term> central to understanding each story in our <term> corpus </term> .	#5773 Annotators generated a list ofquestions central to understanding each story in our corpus.
lr,13-2-H05-1115,ak	<term> Annotators </term> generated a list of <term> questions </term> central to understanding each story in our <term> corpus </term> .	#5781 Annotators generated a list of questions central to understanding each story in ourcorpus.
other,10-3-H05-1115,ak	Because of the dynamic nature of the stories , many <term> questions </term> are time-sensitive ( e.g . How many victims have been found ? )	#5793 Because of the dynamic nature of the stories, manyquestions are time-sensitive (e.g. How many victims have been found?)
other,2-4-H05-1115,ak	Judges found <term> sentences </term> providing an <term> answer </term> to each <term> question </term> .	#5809 Judges foundsentences providing an answer to each question.
other,5-4-H05-1115,ak	Judges found <term> sentences </term> providing an <term> answer </term> to each <term> question </term> .	#5812 Judges found sentences providing ananswer to each question.
other,8-4-H05-1115,ak	Judges found <term> sentences </term> providing an <term> answer </term> to each <term> question </term> .	#5815 Judges found sentences providing an answer to eachquestion.
other,3-5-H05-1115,ak	To address the <term> sentence retrieval problem </term> , we apply a <term> stochastic , graph-based method </term> for comparing the <term> relative importance </term> of the <term> textual units </term> , which was previously used successfully for <term> generic summarization </term> .	#5820 To address thesentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization.
tech,10-5-H05-1115,ak	To address the <term> sentence retrieval problem </term> , we apply a <term> stochastic , graph-based method </term> for comparing the <term> relative importance </term> of the <term> textual units </term> , which was previously used successfully for <term> generic summarization </term> .	#5827 To address the sentence retrieval problem, we apply astochastic , graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization.
measure(ment),17-5-H05-1115,ak	To address the <term> sentence retrieval problem </term> , we apply a <term> stochastic , graph-based method </term> for comparing the <term> relative importance </term> of the <term> textual units </term> , which was previously used successfully for <term> generic summarization </term> .	#5834 To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing therelative importance of the textual units, which was previously used successfully for generic summarization.
other,21-5-H05-1115,ak	To address the <term> sentence retrieval problem </term> , we apply a <term> stochastic , graph-based method </term> for comparing the <term> relative importance </term> of the <term> textual units </term> , which was previously used successfully for <term> generic summarization </term> .	#5838 To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of thetextual units, which was previously used successfully for generic summarization.
tech,30-5-H05-1115,ak	To address the <term> sentence retrieval problem </term> , we apply a <term> stochastic , graph-based method </term> for comparing the <term> relative importance </term> of the <term> textual units </term> , which was previously used successfully for <term> generic summarization </term> .	#5847 To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully forgeneric summarization.
measure(ment),18-6-H05-1115,ak	Currently , we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive <term> baseline </term> , which compares the <term> similarity </term> of each <term> sentence </term> to the <term> input question </term> via <term> IDF-weighted word overlap </term> .	#5868 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitivebaseline, which compares the similarity of each sentence to the input question via IDF-weighted word overlap.
measure(ment),23-6-H05-1115,ak	Currently , we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive <term> baseline </term> , which compares the <term> similarity </term> of each <term> sentence </term> to the <term> input question </term> via <term> IDF-weighted word overlap </term> .	#5873 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares thesimilarity of each sentence to the input question via IDF-weighted word overlap.
other,26-6-H05-1115,ak	Currently , we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive <term> baseline </term> , which compares the <term> similarity </term> of each <term> sentence </term> to the <term> input question </term> via <term> IDF-weighted word overlap </term> .	#5876 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of eachsentence to the input question via IDF-weighted word overlap.
other,29-6-H05-1115,ak	Currently , we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive <term> baseline </term> , which compares the <term> similarity </term> of each <term> sentence </term> to the <term> input question </term> via <term> IDF-weighted word overlap </term> .	#5879 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of each sentence to theinput question via IDF-weighted word overlap.
measure(ment),32-6-H05-1115,ak	Currently , we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive <term> baseline </term> , which compares the <term> similarity </term> of each <term> sentence </term> to the <term> input question </term> via <term> IDF-weighted word overlap </term> .	#5882 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of each sentence to the input question viaIDF-weighted word overlap.
measure(ment),8-7-H05-1115,ak	In our experiments , the method achieves a <term> TRDR score </term> that is significantly higher than that of the <term> baseline </term> .	#5894 In our experiments, the method achieves aTRDR score that is significantly higher than that of the baseline.
measure(ment),18-7-H05-1115,ak	In our experiments , the method achieves a <term> TRDR score </term> that is significantly higher than that of the <term> baseline </term> .	#5904 In our experiments, the method achieves a TRDR score that is significantly higher than that of thebaseline.


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