论文信息 - NER Systems that Suit User’s Preferences: Adjusting the Recall-Precision Trade-off for Entity Extraction

NER Systems that Suit User’s Preferences: Adjusting the Recall-Precision Trade-off for Entity Extraction

We describe a method based on "tweaking" an existing learned sequential classifier to change the recall-precision tradeoff, guided by a user-provided performance criterion. This method is evaluated on the task of recognizing personal names in email and newswire text, and proves to be both simple and effective.

[1] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[2] Michael Collins,et al. Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[3] W. Bruce Croft,et al. Table extraction using conditional random fields , 2003, DG.O.

[4] Fernando Pereira,et al. Shallow Parsing with Conditional Random Fields , 2003, NAACL.

[5] Wei Li,et al. Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons , 2003, CoNLL.

[6] Yiming Yang,et al. Introducing the Enron Corpus , 2004, CEAS.

[7] Andrew McCallum,et al. Confidence Estimation for Information Extraction , 2004, NAACL.

[8] William W. Cohen,et al. Extracting Personal Names from Email: Applying Named Entity Recognition to Informal Text , 2005, HLT.