关键词: 实体识别, 三元组抽取, 联合抽取, 人才履历信息, 关系重叠

Abstract: The field of talent title evaluation contains a large amount of talent resume information， but resume information often exists in the form of natural language， which experts find difficult to use as a basis for talent title evaluation. To address this issue， this article combines entity extraction and relationship extraction for joint modeling， and constructs a triplet joint extraction model （RLAC） for talent resume information. Firstly， the Chinese pre-trained language model RoBERT-wwm is used to encode the underlying talent resume information. Secondly， the introduction of LSTM network and attention mechanism improves the problem of difficult recognition of head entities in talent resume information， and enhances the ability to extract semantic features in coding context. Thirdly， input the encoded information into the header entity annotator to obtain the header entity. Finally， concatenate the head entity and talent resume information and input them into the tail entity relationship annotator to alleviate the problem of relationship overlap， thus obtaining a triplet. Compared with the baseline model, the experimental results on the talent resume dataset of the proposed model has improved accuracy, recall, and F1 value, indicating that the model has good triplet extraction ability.

Key words: entity recognition, triplet extraction, joint extraction, talent resume information, relationship overlap