Concordance

Query N04-1024 20 >
Negative filter essays 19 (885.8 per million)

tool,0-1-N04-1024,bq	specific <term> loss functions </term> . <term>	CriterionSM Online Essay Evaluation Service	</term> includes a capability that labels	#6645 Our results show that MBR decoding can be used to tune statistical MT performance for specific loss functions.CriterionSM Online Essay Evaluation Service includes a capability that labels sentences in student writing with essay-based discourse elements (e.g., thesis statements).
other,10-1-N04-1024,bq	</term> includes a capability that labels <term>	sentences	</term> in student <term> writing </term> with	#6655 CriterionSM Online Essay Evaluation Service includes a capability that labelssentences in student writing with essay-based discourse elements (e.g., thesis statements).
other,13-1-N04-1024,bq	labels <term> sentences </term> in student <term>	writing	</term> with <term> essay-based discourse elements	#6658 CriterionSM Online Essay Evaluation Service includes a capability that labels sentences in studentwriting with essay-based discourse elements (e.g., thesis statements).
other,15-1-N04-1024,bq	in student <term> writing </term> with <term>	essay-based discourse elements	</term> ( e.g. , <term> thesis statements </term>	#6660 CriterionSM Online Essay Evaluation Service includes a capability that labels sentences in student writing withessay-based discourse elements (e.g., thesis statements).
other,21-1-N04-1024,bq	discourse elements </term> ( e.g. , <term>	thesis statements	</term> ) . We describe a new system that	#6666 CriterionSM Online Essay Evaluation Service includes a capability that labels sentences in student writing with essay-based discourse elements (e.g.,thesis statements).
tool,7-2-N04-1024,bq	describe a new system that enhances <term>	Criterion	</term> 's capability , by evaluating multiple	#6677 We describe a new system that enhancesCriterion's capability, by evaluating multiple aspects of coherence in essays.
other,16-2-N04-1024,bq	by evaluating multiple aspects of <term>	coherence	</term> in <term> essays </term> . This system	#6686 We describe a new system that enhances Criterion's capability, by evaluating multiple aspects ofcoherence in essays.
other,3-3-N04-1024,bq	essays </term> . This system identifies <term>	features	</term> of <term> sentences </term> based on <term>	#6693 This system identifiesfeatures of sentences based on semantic similarity measures and discourse structure.
other,5-3-N04-1024,bq	identifies <term> features </term> of <term>	sentences	</term> based on <term> semantic similarity	#6695 This system identifies features ofsentences based on semantic similarity measures and discourse structure.
other,8-3-N04-1024,bq	</term> of <term> sentences </term> based on <term>	semantic similarity measures	</term> and <term> discourse structure </term>	#6698 This system identifies features of sentences based onsemantic similarity measures and discourse structure.
other,12-3-N04-1024,bq	semantic similarity measures </term> and <term>	discourse structure	</term> . A <term> support vector machine </term>	#6702 This system identifies features of sentences based on semantic similarity measures anddiscourse structure.
tech,1-4-N04-1024,bq	<term> discourse structure </term> . A <term>	support vector machine	</term> uses these <term> features </term> to	#6706 Asupport vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements.
other,6-4-N04-1024,bq	support vector machine </term> uses these <term>	features	</term> to capture <term> breakdowns in coherence	#6711 A support vector machine uses thesefeatures to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements.
other,9-4-N04-1024,bq	these <term> features </term> to capture <term>	breakdowns in coherence	</term> due to relatedness to the <term> essay	#6714 A support vector machine uses these features to capturebreakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements.
other,17-4-N04-1024,bq	coherence </term> due to relatedness to the <term>	essay question	</term> and relatedness between <term> discourse	#6722 A support vector machine uses these features to capture breakdowns in coherence due to relatedness to theessay question and relatedness between discourse elements.
other,22-4-N04-1024,bq	question </term> and relatedness between <term>	discourse elements	</term> . <term> Intra-sentential quality </term>	#6727 A support vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness betweendiscourse elements.
other,0-5-N04-1024,bq	between <term> discourse elements </term> . <term>	Intra-sentential quality	</term> is evaluated with <term> rule-based	#6730 A support vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements.Intra-sentential quality is evaluated with rule-based heuristics.
tech,5-5-N04-1024,bq	Intra-sentential quality </term> is evaluated with <term>	rule-based heuristics	</term> . Results indicate that the system	#6735 Intra-sentential quality is evaluated withrule-based heuristics.
other,10-6-N04-1024,bq	system yields higher performance than a <term>	baseline	</term> on all three aspects . <term> Information	#6748 Results indicate that the system yields higher performance than abaseline on all three aspects.


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