Project 1: CRFs for NER
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چکیده
In this assignment, we look at the problem of Named Entity Recognition (NER), which is a sequence labeling task. We approach the problem using chain CRF model. Further, we compare the CRF model to structured SVM, in terms of F1 score, learning curve, training time and variation across multiple runs.
منابع مشابه
Chinese Named Entity Recognition with the Improved Smoothed Conditional Random Fields1
As a kind of state-of-the-art sequence classifier, Conditional Random Fields (CRFs) recently have been widely used for some natural language processing tasks which could be viewed as the sequence labeling problems such as POS tagging, named entity recognition(NER) etc. But CRFs suffer from the failing that they are prone to overfitting when the number of features grows. For NER task, the featur...
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This paper mainly describes a Chinese named entity recognition (NER) system NER@ISCAS, which integrates text, partof-speech and a small-vocabularycharacter-lists feature and heristic postprocess rules for MSRA NER open track under the framework of Conditional Random Fields (CRFs) model.
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We report a high-performance Chinese NER system that incorporates Conditional Random Fields (CRFs) and first-order logic for the fourth SIGHAN Chinese language processing bakeoff (SIGHAN-6). Using current state-of-theart CRFs along with a set of well-engineered features for Chinese NER as the base model, we consider distinct linguistic characteristics in Chinese named entities by introducing va...
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