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