Unformatted text preview: t;/protein> control <DNA> IL-2 receptor alpha (IL-2R alpha)
gene </DNA> transcription in <cell_line> CD4-CD8- murine T lymphocyte
In the 2004 evaluation four types of extraction models were used: Support
Vector Machines (SVMs), Hidden Markov Models (HMMs), Conditional Random
Fields (CRFs) and the related Maximum Entropy Markov Models (MEMMs).
Varying types of input features were employed: lexical features (words), n-grams,
orthographic information, word lists, part-of-speech tags, noun phrase tags,
etc. The evaluation shows that the best ﬁve systems yield an F1-value of about
70% (Kim et al. 2004). They use SVMs in combination with Markov models
(72.6%), MEMMs (70.1%), CRFs (69.8%), CRFs together with SVMs (66.3%),
and HMMs (64.8%). For practical applications the current accuracy levels are
not yet satisfactory and research currently aims at including a sophisticated
mix of external resources such as keyword lists and ontologies which provide
terminological resources. 4.4 Anti-Spa...
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This note was uploaded on 06/19/2011 for the course IT 2258 taught by Professor Aymenali during the Summer '11 term at Abu Dhabi University.
- Summer '11