ACL RD-TEC 1.0 Summarization of A00-2020
Paper Title:
DETECTING ERRORS WITHIN A CORPUS USING ANOMALY DETECTION
DETECTING ERRORS WITHIN A CORPUS USING ANOMALY DETECTION
Primarily assigned technology terms:
- algorithm
- anomaly detection
- automatic method
- boosting
- computer security
- computing
- corpus error detection
- detection framework
- distribution modeling
- error detection
- estimation algorithm
- language processing
- learning
- learning method
- learning technique
- machine learning
- markov transducer estimation
- maximum entropy
- maximum entropy approach
- modeling
- naive bayes
- naive bayes approach
- natural language processing
- normalization
- part of speech tagging
- predictor
- probability distribution modeling
- probability function
- probability modeling
- processing
- ranking
- speech tagging
- statistical test
- taggers
- tagging
- transducer
- transducer probability modeling
- transducers
- transformation-based tagging
Other assigned terms:
- alphabet
- annotation
- annotator
- annotators
- approach
- bayes independence assumption
- bigram
- case
- conditional probability
- conditional probability distribution
- corpora
- data set
- data sets
- distribution
- entropy
- error rate
- estimation
- events
- fact
- finite set
- human annotator
- independence assumption
- knowledge
- large corpora
- likelihood
- mapping
- mappings
- markov chain
- markov models
- measure
- method
- methodology
- n-gram
- n-grams
- naive bayes independence assumption
- natural language
- part of speech
- penn treebank
- penn treebank corpus
- priori
- probabilities
- probability
- probability distribution
- probability distributions
- probability model
- process
- sentence
- sparse data
- statistics
- stems
- style
- symbol
- symbols
- tagged corpus
- tags
- technique
- training
- training data
- treebank
- treebank corpus
- trigram
- word
- words
