| 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 or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. |
| 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 the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. |
| 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 targeted NE , e.g. he/she/man/woman for PERSON NE. |
| 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 a corpus automatically tagged by the first learner. |
| tech,16-5-N03-2025,bq | 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 first learner . |
