ACL RD-TEC 1.0 Summarization of P02-1062
Paper Title:
RANKING ALGORITHMS FOR NAMED ENTITY EXTRACTION: BOOSTING AND THE VOTEDPERCEPTRON
RANKING ALGORITHMS FOR NAMED ENTITY EXTRACTION: BOOSTING AND THE VOTEDPERCEPTRON
Primarily assigned technology terms:
- algorithm
- beam search
- boosting
- boosting algorithm
- capitalization
- classification
- computational linguistics
- decoding
- entity extraction
- error reduction
- generalized iterative scaling
- greedy algorithm
- information extraction
- information extraction tasks
- iterative scaling
- kernels
- learning
- loss function
- markov random field
- matching
- maximum entropy
- maximum-entropy
- named entity extraction
- named-entity extraction
- named-entity recognition
- parsing
- part-of-speech tagging
- perceptron
- perceptron algorithm
- processing
- question-answering
- question-answering system
- ranking
- ranking function
- recognition
- reranking
- search
- segmentation
- tagger
- taggers
- tagging
- voted perceptron
- voted perceptron algorithm
Other assigned terms:
- ambiguity
- annotator
- approach
- array
- baseline model
- beam
- bias
- bigram
- case
- classification problem
- conditional distribution
- development set
- dictionary
- distribution
- entropy
- error rate
- f-measure
- fact
- feature
- feature type
- hmm model
- hypotheses
- implementation
- index
- large corpus
- learning problem
- lexicon
- linguistics
- log-likelihood
- maximum-entropy model
- meaning
- measure
- method
- named entity
- named-entity
- names
- parameter settings
- parameter values
- parse
- part-of-speech
- precision
- probabilities
- probability
- procedure
- ranking candidate
- relative error reduction
- representations
- sentence
- sentences
- sources of information
- sparse data
- symbol
- symbols
- tagging task
- tags
- term
- terms
- test data
- test set
- text
- theory
- training
- training data
- training example
- training examples
- training phase
- training set
- transformation
- trees
- trigram
- web pages
- word
- words
