Abstract
Information extraction (IE) is a textual information processing task concerned with the automatic extraction of entity mentions and relational structures from documents, which has been widely studied and applied in various fields such as biomedicine and news. In this paper, we extend this technique to biographies and propose a new BioIE3 dataset to extract the authors' experiences (including birth, education, work and awards) from their profiles in the scientific literatures. Named entity recognition (NER) and relation extraction (RE) tasks are performed simultaneously in a neural architecture based on the Skip-Gram Word2vec, Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) models. On the basis of the above work, negative examples are especially introduced into the training stage to effectively improve and increase the robustness of the model.
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