ACL RD-TEC 1.0 Summarization of H05-1056
Paper Title:
EXTRACTING PERSONAL NAMES FROM EMAIL: APPLYING NAMED ENTITY RECOGNITION TO INFORMAL TEXT
EXTRACTING PERSONAL NAMES FROM EMAIL: APPLYING NAMED ENTITY RECOGNITION TO INFORMAL TEXT
Authors: Einat Minkov and Richard C. Wang and William W. Cohen
Primarily assigned technology terms:
- capitalization
- classification
- classification approach
- classifier
- collective classification
- computational linguistics
- crf training
- document analysis
- entity extraction
- entity recognition
- extractor
- human language
- human language technology
- information retrieval
- language processing
- language technology
- learner
- learning
- learning methods
- machine-learning
- matching
- modeling
- name matching
- named entity recognition
- natural language processing
- np-chunking
- processing
- quantitative analysis
- recall-enhancing
- recognition
- regular expression
- sampling
- sequential learning
- smoothing
- tagging
- tuning
- validation
Other assigned terms:
- ambiguity
- ambiguous words
- annotated corpora
- annotation
- annotators
- approach
- association for computational linguistics
- baseline performance
- bigram
- case
- co-occurrence
- corpora
- dictionaries
- dictionary
- dictionary entry
- distribution
- document
- document frequency
- email message
- email text
- enron corpus
- entity types
- experimental results
- fact
- feature
- feature set
- feature sets
- heuristic
- inverse document frequency
- labeling
- linguistic
- linguistics
- measure
- message
- method
- name-matching
- named entities
- named entity
- names
- natural language
- parameter settings
- person names
- personal names
- precision
- prefixes and suffixes
- probabilistic approach
- probability
- procedure
- pronoun
- server
- set size
- statistics
- suffixes
- tags
- technique
- technology
- terms
- test corpus
- test data
- test set
- text
- text corpus
- tokens
- training
- training corpus
- training data
- training set
- training set size
- training size
- transformation
- transformation scheme
- user
- word
- word distribution
- word frequency
- words
