论文标题
学习药物处方的语法：临床文本中用药物信息提取的复发神经网络语法
Learning the grammar of drug prescription: recurrent neural network grammars for medication information extraction in clinical texts
论文作者
论文摘要
在这项研究中，我们评估了一种基于神经自上而下的解析器RNNG，用于临床文本中的药物信息提取。我们在法国临床语料库上评估了该模型。任务是提取药物的名称（或药物类别），以及属性，以告知其给药：频率，剂量，持续时间，状况和给药途径。我们比较了RNNG模型，该模型共同确定了实体，事件和与实体，事件和关系单独的Bilstms模型作为基准的模型。我们将SEQ-Bilstms称为关系提取的基线模型，将Bilstms的输出作为实体和事件的输出。同样，我们评估了Seq-Rnng，这是一种混合RNNG模型，将BILSTMS的输出作为实体和事件的额外输出。 RNNG的表现优于SEQ-BILSTM，用于识别复杂关系，平均为88.1 [84.4-91.6]％，而69.9 [64.0-75.4] f-measion。但是，在检测实体上，RNNG往往比基线BILSTM弱，平均为82.4 [80.8-83.8]，而84.1 [84.1-7-85.6]％f-Meture。只有用于检测关系的培训的RNNG往往比RNNG弱，而联合建模目标为87.4％[85.8-88.8]，而88.5％[87.2-89.8]。 SEQ-RNNG与Bilstm的实体（84.0 [82.6-85.4]％F-MEASIE）和RNNG的关系（88.7 [87.4-90.0]％f-measion）。 RNNG在关系上的性能可以通过模型体系结构来解释，该模型体系结构提供了归纳偏差以捕获目标中的层次结构，以及允许RNNG学习更丰富表示形式的联合建模目标。 RNNG对于在医学文本中的实体或事件之间建模的关系有效，其性能与实体和事件检测的Bilstm的性能接近。
In this study, we evaluated the RNNG, a neural top-down transition based parser, for medication information extraction in clinical texts. We evaluated this model on a French clinical corpus. The task was to extract the name of a drug (or a drug class), as well as attributes informing its administration: frequency, dosage, duration, condition and route of administration. We compared the RNNG model that jointly identifies entities, events and their relations with separate BiLSTMs models for entities, events and relations as baselines. We call seq-BiLSTMs the baseline models for relations extraction that takes as extra-input the output of the BiLSTMs for entities and events. Similarly, we evaluated seq-RNNG, a hybrid RNNG model that takes as extra-input the output of the BiLSTMs for entities and events. RNNG outperforms seq-BiLSTM for identifying complex relations, with on average 88.1 [84.4-91.6] % versus 69.9 [64.0-75.4] F-measure. However, RNNG tends to be weaker than the baseline BiLSTM on detecting entities, with on average 82.4 [80.8-83.8] versus 84.1 [82.7-85.6] % F- measure. RNNG trained only for detecting relations tends to be weaker than RNNG with the joint modelling objective, 87.4% [85.8-88.8] versus 88.5% [87.2-89.8]. Seq-RNNG is on par with BiLSTM for entities (84.0 [82.6-85.4] % F-measure) and with RNNG for relations (88.7 [87.4-90.0] % F-measure). The performance of RNNG on relations can be explained both by the model architecture, which provides inductive bias to capture the hierarchy in the targets, and the joint modeling objective which allows the RNNG to learn richer representations. RNNG is efficient for modeling relations between entities or/and events in medical texts and its performances are close to those of a BiLSTM for entity and event detection.
