Concordance

Query N03-2025 20 >
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tech,2-4-N03-2025,bq	First , <term> decision list </term> is used to learn the <term> parsing-based NE rules </term> .	#3347 First,decision list is used to learn the parsing-based NE rules.
model,3-5-N03-2025,bq	Then , a <term> Hidden Markov Model </term> is trained on a <term> corpus </term> automatically tagged by the first <term> learner </term> .	#3361 Then, aHidden Markov Model is trained on a corpus automatically tagged by the first learner.
lr,10-5-N03-2025,bq	Then , a <term> Hidden Markov Model </term> is trained on a <term> corpus </term> automatically tagged by the first <term> learner </term> .	#3368 Then, a Hidden Markov Model is trained on acorpus automatically tagged by the first learner.
tech,15-1-N03-2025,bq	A novel <term> bootstrapping approach </term> to <term> Named Entity ( NE ) tagging </term> using <term> concept-based seeds </term> and <term> successive learners </term> is presented .	#3300 A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds andsuccessive learners is presented.
tech,5-6-N03-2025,bq	The resulting <term> NE system </term> approaches <term> supervised NE </term> performance for some <term> NE types </term> .	#3381 The resulting NE system approachessupervised NE performance for some NE types.
other,6-2-N03-2025,bq	This approach only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that correspond to the <term> concept </term> for the targeted <term> NE </term> , e.g. he/she/man / woman for <term> PERSON NE </term> .	#3311 This approach only requires a fewcommon noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE.
tech,16-5-N03-2025,bq	Then , a <term> Hidden Markov Model </term> is trained on a <term> corpus </term> automatically tagged by the first <term> learner </term> .	#3374 Then, a Hidden Markov Model is trained on a corpus automatically tagged by the firstlearner.
other,26-2-N03-2025,bq	This approach only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that correspond to the <term> concept </term> for the targeted <term> NE </term> , e.g. he/she/man / woman for <term> PERSON NE </term> .	#3331 This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman forPERSON NE.
tech,2-1-N03-2025,bq	A novel <term> bootstrapping approach </term> to <term> Named Entity ( NE ) tagging </term> using <term> concept-based seeds </term> and <term> successive learners </term> is presented .	#3287 A novelbootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented.
other,9-2-N03-2025,bq	This approach only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that correspond to the <term> concept </term> for the targeted <term> NE </term> , e.g. he/she/man / woman for <term> PERSON NE </term> .	#3314 This approach only requires a few common noun orpronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE.
other,10-2-N03-2025,bq	This approach only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that correspond to the <term> concept </term> for the targeted <term> NE </term> , e.g. he/she/man / woman for <term> PERSON NE </term> .	#3315 This approach only requires a few common noun or pronounseeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE.
tech,2-6-N03-2025,bq	The resulting <term> NE system </term> approaches <term> supervised NE </term> performance for some <term> NE types </term> .	#3378 The resultingNE system approaches supervised NE performance for some NE types.
other,10-6-N03-2025,bq	The resulting <term> NE system </term> approaches <term> supervised NE </term> performance for some <term> NE types </term> .	#3386 The resulting NE system approaches supervised NE performance for someNE types.
other,19-2-N03-2025,bq	This approach only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that correspond to the <term> concept </term> for the targeted <term> NE </term> , e.g. he/she/man / woman for <term> PERSON NE </term> .	#3324 This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targetedNE, e.g. he/she/man/woman for PERSON NE.
tech,1-3-N03-2025,bq	The <term> bootstrapping procedure </term> is implemented as training two <term> successive learners </term> .	#3335 Thebootstrapping procedure is implemented as training two successive learners.
model,9-4-N03-2025,bq	First , <term> decision list </term> is used to learn the <term> parsing-based NE rules </term> .	#3354 First, decision list is used to learn theparsing-based NE rules.
other,15-2-N03-2025,bq	This approach only requires a few <term> common noun </term> or <term> pronoun </term><term> seeds </term> that correspond to the <term> concept </term> for the targeted <term> NE </term> , e.g. he/she/man / woman for <term> PERSON NE </term> .	#3320 This approach only requires a few common noun or pronoun seeds that correspond to theconcept for the targeted NE, e.g. he/she/man/woman for PERSON NE.
tech,5-1-N03-2025,bq	A novel <term> bootstrapping approach </term> to <term> Named Entity ( NE ) tagging </term> using <term> concept-based seeds </term> and <term> successive learners </term> is presented .	#3290 A novel bootstrapping approach toNamed Entity ( NE ) tagging using concept-based seeds and successive learners is presented.
tech,8-3-N03-2025,bq	The <term> bootstrapping procedure </term> is implemented as training two <term> successive learners </term> .	#3342 The bootstrapping procedure is implemented as training twosuccessive learners.
other,12-1-N03-2025,bq	A novel <term> bootstrapping approach </term> to <term> Named Entity ( NE ) tagging </term> using <term> concept-based seeds </term> and <term> successive learners </term> is presented .	#3297 A novel bootstrapping approach to Named Entity (NE) tagging usingconcept-based seeds and successive learners is presented.


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