A Maximum Entropy Approach to Named Entity Recognition

By Ralph Grishman, Andrew Borthwick. 1999

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Citations in the corpus

Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition (2003, background): “There has been a lot of work on named entity recognition, especially for English (see Borthwick (1999) for an overview).”
Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling (2005, background): “Statistical hidden state sequence models, such as Hidden Markov Models (HMMs) (Leek, 1997; Freitag and McCallum, 1999), Conditional Markov Models (CMMs) (Borthwick, 1999), and Conditional Random Fields (CRFs) (Lafferty et al.”
Named Entity Recognition: A Maximum Entropy Approach Using Global Information (2002, methodology): “These results are achieved by training on the official MUC-6 and MUC-7 training data, which is much less training data than is used by other machine learning systems that worked on the MUC-6 or MUC-7 named entity task (Bikel et al., 1997; Bikel et al., 1999; Borthwick, 1999).”
Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2021, background): “4), ranging from summarization to entity typing, a subproblem of entity recognition (Grishman and Borthwick, 1999).”
Named Entity Recognition through Classifier Combination (2003, unknown): “, 2002) (henceforth RRM) and a maximum entropy classifier (Darroch and Ratcliff, 1972; Berger et al., 1996; Borthwick, 1999) (henceforth MaxEnt).”
Named entity recognition in query (2009, methodology): “Traditionally Named Entity Recognition (NER) is mainly performed on natural language texts [6, 3, 8].”
Efficient Support Vector Classifiers for Named Entity Recognition (2002, background): “Now, Japanese NE recognition is solved by the classification of words (Sekine et al., 1998; Borthwick, 1999; Uchimoto et al., 2000).”
Nested Named Entity Recognition (2009, unknown): “This then led to the widespread use of sequence models, first hidden Markov models, then conditional Markov models (Borthwick, 1999), and, more recently, linear chain conditional random fields (CRFs) (Lafferty et al.”