RULE LEARNING: Related Papers in ACL Anthology

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

• (ACL ID: C00-2175) comparing two trainable grammatical relations finders
• (ACL ID: C04-1005) improving statistical word alignment with a rule-based machine translation system
• (ACL ID: C04-1027) learning theories from text
• (ACL ID: C04-1058) why nitpicking works
• (ACL ID: C90-2074) morphological analysis and synthesis by automated discovery and acquisition of linguistic rules
• (ACL ID: C96-1047) finite-state phrase parsing by rule sequences
• (ACL ID: H05-2019) posbiotm/w
• (ACL ID: J01-1003) bootstrapping morphological analyzers by combining human elicitation and machine learning
• (ACL ID: J01-3003) automatic verb classification based on statistical distributions of argument structure
• (ACL ID: N03-2030) a hybrid approach to content analysis for automatic essay grading
• (ACL ID: N04-1018) detecting structural metadata with decision trees and transformation-based learning
• (ACL ID: P01-1038) generation of vp ellipsis
• (ACL ID: P01-1049) building semantic perceptron net for topic spotting
• (ACL ID: P01-1055) using machine learning to maintain rule-based named-entity recognition and classification systems
• (ACL ID: P02-1014) improving machine learning approaches to coreference resolution
• (ACL ID: P03-1043) a bootstrapping approach to named entity classification using successive learners
• (ACL ID: P03-1071) discourse segmentation of multi-party conversation
• (ACL ID: P04-1020) learning noun phrase anaphoricity to improve conference resolution
• (ACL ID: P05-1020) machine learning for coreference resolution
• (ACL ID: P05-2015) learning strategies for open-domain natural language question answering
• (ACL ID: P05-3025) interactively exploring a machine translation model
• (ACL ID: P06-2100) morphological richness offsets resource demand – experiences in constructing a pos tagger for hindi
• (ACL ID: P98-1051) error driven word sense disambiguation
• (ACL ID: P98-2186) part of speech tagging using a network of linear separators
• (ACL ID: W01-0719) combining linguistic and machine learning techniques for email summarization
• (ACL ID: W01-1011) gist-it
• (ACL ID: W01-1603) development of a machine learnable discourse tagging tool
• (ACL ID: W02-0221) training a dialogue act tagger for human-human and human-computer travel dialogues
• (ACL ID: W02-1008) combining sample selection and error-driven pruning for machine learning of coreference rules
• (ACL ID: W02-1031) the superarv language model
• (ACL ID: W02-1605) improving translation quality of rule-based machine translation
• (ACL ID: W02-1819) learning rules for chinese prosodic phrase prediction
• (ACL ID: W02-2023) named entity learning and verification
• (ACL ID: W03-0202) learning to identify student preconceptions from text
• (ACL ID: W03-0210) a hybrid text classification approach for analysis of student essays
• (ACL ID: W03-0433) a stacked, voted, stacked model for named entity recognition
• (ACL ID: W03-1300) proceedings of the acl 2003 workshop on natural language processing in biomedicine
• (ACL ID: W03-1303) using domain-specific verbs for term classification
• (ACL ID: W03-1724) integrating ngram model and case-based learning for chinese word segmentation
• (ACL ID: W04-0505) biographer
• (ACL ID: W04-2803) a little goes a long way
• (ACL ID: W05-0610) using uneven margins svm and perceptron for information extraction
• (ACL ID: W06-1640) partially supervised coreference resolution for opinion summarization through structured rule learning
• (ACL ID: W06-2610) an ontology-based approach to disambiguation of semantic relations
• (ACL ID: W95-0101) unsupervised learning of disambiguation rules for part of speech tagging
• (ACL ID: W96-0207) combining hand-crafted rules and unsupervised learning in constraint-based morphological disambiguation
• (ACL ID: W99-0630) automatically merging lexicons that have incompatible part-of-speech categories
• (ACL ID: W99-0703) practical bootstrapping of morphological analyzers