| tech,2-1-N03-2025,ak | 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 . | #3288 A novelbootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. | |
| tech,1-3-N03-2025,ak | The <term> bootstrapping procedure </term> is implemented as training two <term> successive learners </term> . | #3336 Thebootstrapping procedure is implemented as training two successive learners. | |
| other,15-2-N03-2025,ak | This approach only requires a few common <term> noun or pronoun 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> . | #3321 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. | |
| model,12-1-N03-2025,ak | 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 . | #3298 A novel bootstrapping approach to Named Entity (NE) tagging usingconcept-based seeds and successive learners is presented. | |
| lr,10-5-N03-2025,ak | Then , a <term> Hidden Markov Model </term> is trained on a <term> corpus </term> automatically tagged by the first <term> learner </term> . | #3369 Then, a Hidden Markov Model is trained on acorpus automatically tagged by the first learner. | |
| tech,2-4-N03-2025,ak | First , <term> decision list </term> is used to learn the <term> parsing-based NE rules </term> . | #3348 First,decision list is used to learn the parsing-based NE rules. | |
| tech,3-5-N03-2025,ak | Then , a <term> Hidden Markov Model </term> is trained on a <term> corpus </term> automatically tagged by the first <term> learner </term> . | #3362 Then, aHidden Markov Model is trained on a corpus automatically tagged by the first learner. | |
| tech,16-5-N03-2025,ak | Then , a <term> Hidden Markov Model </term> is trained on a <term> corpus </term> automatically tagged by the first <term> learner </term> . | #3375 Then, a Hidden Markov Model is trained on a corpus automatically tagged by the firstlearner. | |
| tech,5-1-N03-2025,ak | 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 . | #3291 A novel bootstrapping approach toNamed Entity ( NE ) tagging using concept-based seeds and successive learners is presented. | |
| other,19-2-N03-2025,ak | This approach only requires a few common <term> noun or pronoun 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> . | #3325 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,2-6-N03-2025,ak | The resulting <term> NE system </term> approaches <term> supervised NE performance </term> for some <term> NE types </term> . | #3379 The resultingNE system approaches supervised NE performance for some NE types. | |
| other,10-6-N03-2025,ak | The resulting <term> NE system </term> approaches <term> supervised NE performance </term> for some <term> NE types </term> . | #3387 The resulting NE system approaches supervised NE performance for someNE types. | |
| model,7-2-N03-2025,ak | This approach only requires a few common <term> noun or pronoun 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> . | #3313 This approach only requires a few commonnoun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. | |
| model,9-4-N03-2025,ak | First , <term> decision list </term> is used to learn the <term> parsing-based NE rules </term> . | #3355 First, decision list is used to learn theparsing-based NE rules. | |
| other,26-2-N03-2025,ak | This approach only requires a few common <term> noun or pronoun 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> . | #3332 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,15-1-N03-2025,ak | 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 . | #3301 A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds andsuccessive learners is presented. | |
| tech,8-3-N03-2025,ak | The <term> bootstrapping procedure </term> is implemented as training two <term> successive learners </term> . | #3343 The bootstrapping procedure is implemented as training twosuccessive learners. | |
| measure(ment),5-6-N03-2025,ak | The resulting <term> NE system </term> approaches <term> supervised NE performance </term> for some <term> NE types </term> . | #3382 The resulting NE system approachessupervised NE performance for some NE types. |
