FEATURE SELECTION ALGORITHM: Related Papers in ACL Anthology

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

• (ACL ID: H90-1062) improved acoustic modeling for continuous speech recognition
• (ACL ID: I05-3005) morphological features help pos tagging of unknown words across language varieties
• (ACL ID: J96-1002) a maximum entropy approach to natural language processing
• (ACL ID: M91-1035) data extraction as text categorization
• (ACL ID: N03-1023) weakly supervised natural language learning without redundant views
• (ACL ID: N03-2012) detection of agreement vs. disagreement in meetings
• (ACL ID: N03-3007) word fragments identification using acoustic-prosodic features in conversational speech
• (ACL ID: P04-1085) identifying agreement and disagreement in conversational speech
• (ACL ID: P06-1071) a progressive feature selection algorithm for ultra large feature spaces
• (ACL ID: P06-2054) exploiting non-local features for spoken language understanding
• (ACL ID: P06-2084) combining association measures for collocation extraction
• (ACL ID: P98-1080) tagging inlective languages
• (ACL ID: P98-2140) feature lattices for maximum entropy modelling
• (ACL ID: P98-2191) maximum entropy model learning of the translation rules
• (ACL ID: P98-2214) general-to-specific model selection for subcategorization preference
• (ACL ID: W03-1020) a fast algorithm for feature selection in conditional maximum entropy modeling
• (ACL ID: W04-3237) adaptation of maximum entropy capitalizer
• (ACL ID: W06-1617) semantic role labeling of nombank
• (ACL ID: W06-1668) competitive generative models with structure learning for nlp classification tasks
• (ACL ID: W97-0121) collocation lattices and maximum entropy models
• (ACL ID: W97-0123) maximum entropy model learning of subcategorization preference
• (ACL ID: W98-1118) exploiting diverse knowledge sources via maximum entropy in named entity recognition