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Improving the scalability of semi-markov conditional random fields for named entity recognition
- In Proceedings of ACL 2006
, 2006
"... This paper presents techniques to apply semi-CRFs to Named Entity Recognition tasks with a tractable computational cost. Our framework can handle an NER task that has long named entities and many labels which increase the computational cost. To reduce the computational cost, we propose two technique ..."
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Cited by 25 (6 self)
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This paper presents techniques to apply semi-CRFs to Named Entity Recognition tasks with a tractable computational cost. Our framework can handle an NER task that has long named entities and many labels which increase the computational cost. To reduce the computational cost, we propose two techniques: the first is the use of feature forests, which enables us to pack feature-equivalent states, and the second is the introduction of a filtering process which significantly reduces the number of candidate states. This framework allows us to use a rich set of features extracted from the chunk-based representation that can capture informative characteristics of entities. We also introduce a simple trick to transfer information about distant entities by embedding label information into non-entity labels. Experimental results show that our model achieves an F-score of 71.48 % on the JNLPBA 2004 shared task without using any external resources or post-processing techniques. 1
Cascading classifiers for named entities recognition in clinical notes
- In: Workshop Biomedical Information Extraction. 2009
"... Clinical named entities convey great deal of knowledge in clinical notes. This paper investigates named entity recognition from clinical notes using machine learning approaches. We present a cascading system that uses a Conditional Random Fields model, a Support Vector Machine and a Maximum Entropy ..."
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Cited by 8 (0 self)
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Clinical named entities convey great deal of knowledge in clinical notes. This paper investigates named entity recognition from clinical notes using machine learning approaches. We present a cascading system that uses a Conditional Random Fields model, a Support Vector Machine and a Maximum Entropy to reclassify the identified entities in order to reduce misclassification. Voting strategy was employed to determine the class of the recognised entities between the three classifiers. The experiments were conducted on a corpus of 311 manually annotated admission summaries form an Intensive Care Unit. The recognition of 10 types of clinical named entities using 10 fold cross-validation achieved an overall results of 83.3 F-score. The reclassifier effectively increased the performance over stand-alone CRF models by 3.35 F-score.
An Online Cascaded Approach to Biomedical Named Entity Recognition ∗
"... We present an online cascaded approach to biomedical named entity recognition. This approach uses an online training method to substantially reduce the training time required and a cascaded framework to relax the memory requirement. We conduct detailed experiments on the BioNLP dataset from the JNLP ..."
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Cited by 1 (0 self)
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We present an online cascaded approach to biomedical named entity recognition. This approach uses an online training method to substantially reduce the training time required and a cascaded framework to relax the memory requirement. We conduct detailed experiments on the BioNLP dataset from the JNLPBA shared task and compare the results with other systems and published works. Our experimental results show that our approach achieves comparable performance with great reductions in time and space requirements. 1
