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We propose an 'end-to-end' character-based recurrent neural network thatextracts disease named entities from a Japanese medical text and simultaneouslyjudges its modality as either positive or negative; i.e., the mentioned diseaseor symptom is affirmed or negated. The motivation to adopt neural networks isto learn effective lexical and structural representation features for EntityRecognition and also for Positive/Negative classification from an annotatedcorpora without explicitly providing any rule-based or manual feature sets. Weconfirmed the superiority of our method over previous char-based CRF or SVMmethods in the results.

Neural Disease Named Entity Extraction with Character-based BiLSTM+CRF in Japanese Medical Text