ACL RD-TEC 1.0 Summarization of W03-0431
Paper Title:
META-LEARNING ORTHOGRAPHIC AND CONTEXTUAL MODELS FOR LANGUAGE INDEPENDENT NAMED ENTITY RECOGNITION
META-LEARNING ORTHOGRAPHIC AND CONTEXTUAL MODELS FOR LANGUAGE INDEPENDENT NAMED ENTITY RECOGNITION
Authors: Robert Munro and Daren Ler and Jon Patrick
Primarily assigned technology terms:
- algorithm
- bagging
- bootstrap
- capitalization
- case restoration
- chunking
- classification
- classification system
- classifier
- classifiers
- clustering
- computational inference
- entity classification
- entity recognition
- feature representation
- learner
- learning
- learning algorithm
- learning techniques
- machine learner
- machine learning
- machine learning algorithm
- meta-learning
- modeling
- modelling
- n-gram modelling
- named entity classification
- named entity recognition
- ne recognition
- preprocessing
- processing
- recognition
- search
- unsupervised learning
- voting
- voting scheme
- weighted voting
- weighted voting scheme
Other assigned terms:
- bag of words
- case
- case information
- characters
- collocation
- composition
- compound words
- content words
- context window
- contextual features
- contextual information
- data set
- data sets
- development set
- dimensionality
- distribution
- entity types
- fact
- feature
- feature space
- foreign language
- foreign word
- foreign words
- gazetteer
- generalisation
- intention
- knowledge
- lemma
- linguistic
- linguistic phenomena
- measures
- meta-data
- method
- methodology
- n-gram
- n-grams
- named entities
- named entity
- names
- orthographic information
- orthographic structure
- part-of-speech
- phoneme
- phrase
- precision
- prepositions
- probability
- probability distribution
- probability distributions
- punctuation
- representations
- sentence
- sentence position
- stem
- suffix
- suffixes
- tags
- technique
- terms
- test set
- topic shift
- training
- training instance
- training set
- word
- word boundaries
- word level
- words
