SAMPLE SELECTION: Related Papers in ACL Anthology

SAMPLE SELECTION can be found in the following ACL ARC 1.0 documents (click to explore):

• (ACL ID: H01-1026) facilitating treebank annotation using a statistical parser
• (ACL ID: H01-1052) mitigating the paucity-of-data problem
• (ACL ID: H05-1108) cross-linguistic projection of role-semantic information
• (ACL ID: J03-3002) articles the web as a parallel corpus
• (ACL ID: J04-3001) sample selection for statistical parsing
• (ACL ID: N01-1023) applying co-training methods to statistical parsing
• (ACL ID: N03-1031) example selection for bootstrapping statistical parsers
• (ACL ID: N04-1012) ensemble-based active learning for parse selection
• (ACL ID: N06-2018) mmr-based active machine learning for bio named entity recognition
• (ACL ID: P01-1005) scaling to very very large corpora for natural language disambiguation
• (ACL ID: P02-1016) active learning for statistical natural language parsing
• (ACL ID: P03-1014) integrated shallow and deep parsing
• (ACL ID: P04-1075) multi-criteria-based active learning for named entity recognition
• (ACL ID: P06-1142) learning transliteration lexicons from the web
• (ACL ID: P96-1042) minimizing manual annotation cost in supervised training from corpora
• (ACL ID: W00-1303) japanese dependency structure analysis based on support vector machines
• (ACL ID: W00-1306) sample selection for statistical grammar induction
• (ACL ID: W02-1008) combining sample selection and error-driven pruning for machine learning of coreference rules
• (ACL ID: W03-0403) active learning for hpsg parse selection
• (ACL ID: W03-0408) updating an nlp system to fit new domains
• (ACL ID: W05-0619) investigating the effects of selective sampling on the annotation task
• (ACL ID: W06-1660) empirical study on the performance stability of named entity recognition model across domains
• (ACL ID: W06-2901) a mission for computational natural language learning
• (ACL ID: W97-0201) getting serious about word sense disambiguation