Concordance

Query N04-4028 20 >
Shuffle 20 (932.4 per million)

other,17-1-N04-4028,bq	<term> Information extraction techniques </term> automatically create <term> structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term> or <term> newswire documents </term> .	#6771 Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web ornewswire documents.
tech,19-4-N04-4028,bq	The <term> information extraction system </term> we evaluate is based on a <term> linear-chain conditional random field ( CRF ) </term> , a <term> probabilistic model </term> which has performed well on <term> information extraction tasks </term> because of its ability to capture arbitrary , overlapping <term> features </term> of the input in a <term> Markov model </term> .	#6830 The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), aprobabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model.
other,26-4-N04-4028,bq	The <term> information extraction system </term> we evaluate is based on a <term> linear-chain conditional random field ( CRF ) </term> , a <term> probabilistic model </term> which has performed well on <term> information extraction tasks </term> because of its ability to capture arbitrary , overlapping <term> features </term> of the input in a <term> Markov model </term> .	#6837 The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well oninformation extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model.
other,10-5-N04-4028,bq	We implement several techniques to estimate the <term> confidence </term> of both <term> extracted fields </term> and entire <term> multi-field records </term> , obtaining an <term> average precision </term> of 98 % for retrieving correct <term> fields </term> and 87 % for <term> multi-field records </term> .	#6868 We implement several techniques to estimate the confidence of bothextracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.
other,7-5-N04-4028,bq	We implement several techniques to estimate the <term> confidence </term> of both <term> extracted fields </term> and entire <term> multi-field records </term> , obtaining an <term> average precision </term> of 98 % for retrieving correct <term> fields </term> and 87 % for <term> multi-field records </term> .	#6865 We implement several techniques to estimate theconfidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.
other,14-5-N04-4028,bq	We implement several techniques to estimate the <term> confidence </term> of both <term> extracted fields </term> and entire <term> multi-field records </term> , obtaining an <term> average precision </term> of 98 % for retrieving correct <term> fields </term> and 87 % for <term> multi-field records </term> .	#6872 We implement several techniques to estimate the confidence of both extracted fields and entiremulti-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.
measure(ment),19-5-N04-4028,bq	We implement several techniques to estimate the <term> confidence </term> of both <term> extracted fields </term> and entire <term> multi-field records </term> , obtaining an <term> average precision </term> of 98 % for retrieving correct <term> fields </term> and 87 % for <term> multi-field records </term> .	#6877 We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining anaverage precision of 98% for retrieving correct fields and 87% for multi-field records.
other,5-1-N04-4028,bq	<term> Information extraction techniques </term> automatically create <term> structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term> or <term> newswire documents </term> .	#6759 Information extraction techniques automatically createstructured databases from unstructured data sources, such as the Web or newswire documents.
other,12-3-N04-4028,bq	For many reasons , it is highly desirable to accurately estimate the <term> confidence </term> the system has in the correctness of each <term> extracted field </term> .	#6799 For many reasons, it is highly desirable to accurately estimate theconfidence the system has in the correctness of each extracted field.
tech,10-4-N04-4028,bq	The <term> information extraction system </term> we evaluate is based on a <term> linear-chain conditional random field ( CRF ) </term> , a <term> probabilistic model </term> which has performed well on <term> information extraction tasks </term> because of its ability to capture arbitrary , overlapping <term> features </term> of the input in a <term> Markov model </term> .	#6821 The information extraction system we evaluate is based on alinear-chain conditional random field ( CRF ), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model.
other,15-1-N04-4028,bq	<term> Information extraction techniques </term> automatically create <term> structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term> or <term> newswire documents </term> .	#6769 Information extraction techniques automatically create structured databases from unstructured data sources, such as theWeb or newswire documents.
other,21-3-N04-4028,bq	For many reasons , it is highly desirable to accurately estimate the <term> confidence </term> the system has in the correctness of each <term> extracted field </term> .	#6808 For many reasons, it is highly desirable to accurately estimate the confidence the system has in the correctness of eachextracted field.
other,32-5-N04-4028,bq	We implement several techniques to estimate the <term> confidence </term> of both <term> extracted fields </term> and entire <term> multi-field records </term> , obtaining an <term> average precision </term> of 98 % for retrieving correct <term> fields </term> and 87 % for <term> multi-field records </term> .	#6890 We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% formulti-field records.
other,8-1-N04-4028,bq	<term> Information extraction techniques </term> automatically create <term> structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term> or <term> newswire documents </term> .	#6762 Information extraction techniques automatically create structured databases fromunstructured data sources, such as the Web or newswire documents.
tech,0-1-N04-4028,bq	Results indicate that the system yields higher performance than a <term> baseline </term> on all three aspects . <term> Information extraction techniques </term> automatically create <term> structured databases </term> from <term> unstructured data sources </term> , such as the <term> Web </term> or <term> newswire documents </term> .	#6754 Results indicate that the system yields higher performance than a baseline on all three aspects.Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents.
other,38-4-N04-4028,bq	The <term> information extraction system </term> we evaluate is based on a <term> linear-chain conditional random field ( CRF ) </term> , a <term> probabilistic model </term> which has performed well on <term> information extraction tasks </term> because of its ability to capture arbitrary , overlapping <term> features </term> of the input in a <term> Markov model </term> .	#6849 The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlappingfeatures of the input in a Markov model.
measure(ment),7-2-N04-4028,bq	Despite the successes of these systems , <term> accuracy </term> will always be imperfect .	#6781 Despite the successes of these systems,accuracy will always be imperfect.
other,27-5-N04-4028,bq	We implement several techniques to estimate the <term> confidence </term> of both <term> extracted fields </term> and entire <term> multi-field records </term> , obtaining an <term> average precision </term> of 98 % for retrieving correct <term> fields </term> and 87 % for <term> multi-field records </term> .	#6885 We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correctfields and 87% for multi-field records.
model,44-4-N04-4028,bq	The <term> information extraction system </term> we evaluate is based on a <term> linear-chain conditional random field ( CRF ) </term> , a <term> probabilistic model </term> which has performed well on <term> information extraction tasks </term> because of its ability to capture arbitrary , overlapping <term> features </term> of the input in a <term> Markov model </term> .	#6855 The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in aMarkov model.
tech,1-4-N04-4028,bq	The <term> information extraction system </term> we evaluate is based on a <term> linear-chain conditional random field ( CRF ) </term> , a <term> probabilistic model </term> which has performed well on <term> information extraction tasks </term> because of its ability to capture arbitrary , overlapping <term> features </term> of the input in a <term> Markov model </term> .	#6812 Theinformation extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model.


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