Conditional Random Fields (CRF): Short Survey

For example, some Indian researchers used CRF to extract key words from medical texts and they had good features and large enough training sample, but they obtained quality not more than 0.4 (F1-measure). On real data they would hardly obtain such quality, while Stanford NER shows quality not more than 0.81 (F-measure) given it has perfectly selected training features and it was trained on larger corpora (CoNLL, MUC-6, MUC-7 and ACE) Some Spanish and Russian researchers compared HMM and CRF in NER task for medical texts on JNLPBA corpus (18546 sentences with 109588 named entities). They obtained interesting results: HMM had higher recall ( 4-7% depending on the type of entity) while CRF had higher precision ( 4-13% depending on the type of entity). According to one master thesis, linear-chain CRF operated very well on extracting time expressions from Russian text.