Structure features need complicated pre-processing, and are probably domain-dependent. To reduce time cost of pre-processing, we propose a novel neural network architecture which is a bi-directional long-short-term-me...Structure features need complicated pre-processing, and are probably domain-dependent. To reduce time cost of pre-processing, we propose a novel neural network architecture which is a bi-directional long-short-term-memory recurrent-neural-network（Bi-LSTM-RNN） model based on low-cost sequence features such as words and part-of-speech（POS） tags, to classify the relation of two entities. First, this model performs bi-directional recurrent computation along the tokens of sentences. Then, the sequence is divided into five parts and standard pooling functions are applied over the token representations of each part. Finally, the token representations are concatenated and fed into a softmax layer for relation classification. We evaluate our model on two standard benchmark datasets in different domains, namely Sem Eval-2010 Task 8 and Bio NLP-ST 2016 Task BB3. In Sem Eval-2010 Task 8, the performance of our model matches those of the state-of-the-art models, achieving 83.0% in F1. In Bio NLP-ST 2016 Task BB3, our model obtains F1 51.3% which is comparable with that of the best system. Moreover, we find that the context between two target entities plays an important role in relation classification and it can be a replacement of the shortest dependency path.展开更多
文摘1病例摘要患者,男性,68岁,无吸烟史,无肿瘤家族史。患者于2024-10-27因咳嗽、咳痰在哈尔滨医科大学附属肿瘤医院行胸部计算机断层扫描(computedtomography,CT)提示右肺门占位性病变,大小约45 mm×54 mm,考虑肺癌可能。2024-10-28在我院行支气管镜检查提示右中间支气管可见新生物,阻塞右肺中叶开口,表面血运丰富,右肺下叶开口受压狭窄。2024-10-29行支气管镜检查,于右中间支气管镜下钳夹组织送检,病理示:(右主支气管)鳞状细胞癌。辅助检查未见远处转移灶。诊断为右肺鳞癌cT3NxM0。美国东部肿瘤协作组体能状态(Eastern Cooperative Oncology Group performancestatus,ECOG PS)评分为1分。基于患者本人决定,未送检程序性死亡配体1(programmed death ligand1,PD-L1)及基因检测。
基金Supported by the China Postdoctoral Science Foundation(2014T70722)the Humanities and Social Science Foundation of Ministry of Education of China(16YJCZH004)
文摘Structure features need complicated pre-processing, and are probably domain-dependent. To reduce time cost of pre-processing, we propose a novel neural network architecture which is a bi-directional long-short-term-memory recurrent-neural-network（Bi-LSTM-RNN） model based on low-cost sequence features such as words and part-of-speech（POS） tags, to classify the relation of two entities. First, this model performs bi-directional recurrent computation along the tokens of sentences. Then, the sequence is divided into five parts and standard pooling functions are applied over the token representations of each part. Finally, the token representations are concatenated and fed into a softmax layer for relation classification. We evaluate our model on two standard benchmark datasets in different domains, namely Sem Eval-2010 Task 8 and Bio NLP-ST 2016 Task BB3. In Sem Eval-2010 Task 8, the performance of our model matches those of the state-of-the-art models, achieving 83.0% in F1. In Bio NLP-ST 2016 Task BB3, our model obtains F1 51.3% which is comparable with that of the best system. Moreover, we find that the context between two target entities plays an important role in relation classification and it can be a replacement of the shortest dependency path.
