ITERATIVE PROCEDURE: Related Papers in ACL Anthology

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

• (ACL ID: C04-1091) an algorithmic framework for solving the decoding problem in statistical machine translation
• (ACL ID: C80-1070) a trial of japanese text input system using speech recognition
• (ACL ID: C92-2099) acquisition of selectional patterns
• (ACL ID: E87-1010) pattern recognition applied to the acquisition of a grammatical classification system from unrestricted english text
• (ACL ID: H01-1059) portability issues for speech recognition technologies
• (ACL ID: H01-1074) the use of dynamic segment scoring for language-independent question answering
• (ACL ID: H05-1043) extracting product features and opinions from reviews
• (ACL ID: H90-1053) statistical parsing of messages
• (ACL ID: H90-1069) towards understanding text with a very large vocabulary
• (ACL ID: H91-1025) a statistical approach to sense disambiguation in machine translation
• (ACL ID: H92-1036) map estimation of continuous density hmm
• (ACL ID: J04-3001) sample selection for statistical parsing
• (ACL ID: J05-2005) representing discourse coherence
• (ACL ID: J05-4005) chinese word segmentation and named entity recognition
• (ACL ID: J93-2003) the mathematics of statistical machine translation
• (ACL ID: J94-2001) tagging english text with a probabilistic model
• (ACL ID: J97-3004) an efficient implementation of the head-corner parser
• (ACL ID: N03-1001) effective utterance classification with unsupervised phonotactic models
• (ACL ID: N03-2007) active learning for classifying phone sequences from unsupervised phonotactic models
• (ACL ID: N04-2003) maximum entropy modeling in sparse semantic tagging
• (ACL ID: N06-1025) exploiting semantic role labeling, wordnet and wikipedia for coreference resolution
• (ACL ID: N06-1033) synchronous binarization for machine translation
• (ACL ID: P01-1055) using machine learning to maintain rule-based named-entity recognition and classification systems
• (ACL ID: P03-1022) a machine learning approach to pronoun resolution in spoken dialogue
• (ACL ID: P05-3015) syntax-based semi-supervised named entity tagging
• (ACL ID: P06-1012) estimating class priors in domain adaptation for word sense disambiguation
• (ACL ID: P06-1085) contextual dependencies in unsupervised word segmentation
• (ACL ID: P06-1136) reranking answers for definitional qa using language modeling
• (ACL ID: P94-1004) hidden understanding models of natural language
• (ACL ID: P96-1019) an iterative algorithm to build chinese language models
• (ACL ID: P97-1024) independence assumptions considered harmful
• (ACL ID: P98-2162) improving statistical natural language translation with categories and rules
• (ACL ID: W02-0606) unsupervised discovery of morphologically related words based on orthographic and semantic similarity
• (ACL ID: W02-0706) architectures for speech-to-speech translation using finite-state models
• (ACL ID: W03-0305) reducing parameter space for word alignment
• (ACL ID: W03-0317) acquisition of english-chinese transliterated word pairs from parallel-aligned texts using a statistical machine transliteration model
• (ACL ID: W03-0423) named entity recognition with a maximum entropy approach
• (ACL ID: W04-0703) event clustering on streaming news using co-reference chains and event words
• (ACL ID: W04-3003) interactive machine learning techniques for improving slu models
• (ACL ID: W06-3604) all-word prediction as the ultimate confusible disambiguation
• (ACL ID: W97-0309) aggregate and mixed-order markov models for statistical language processing
• (ACL ID: W97-0407) using categories in the eutrans system
• (ACL ID: W97-1013) explanation-based learning of data-oriented parsing