To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints

End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures

We present a novel end-to-end neural model to extract entities and relations between them. Our recurrent neural network based model captures both word sequence and dependency tree substructure information by stacking bidirectional tree-structured LSTM-RNNs on bidirectional sequential LSTM-RNNs. This allows our model to jointly represent both entities and relations with shared parameters in a single model. We further encourage detection of entities during training and use of entity information in relation extraction via entity pretraining and scheduled sampling. Our model improves over the stat…

Open Named Entity Modeling from Embedding Distribution

In this paper, we report our discovery on named entity distribution in a general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedule update. Our initial visualization of monolingual word embeddings indicates named entities tend to gather together despite of named entity types and language difference, which enable us to model all named entities using a specific geometric structure ins…

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Query embedding (QE) -- which aims to embed entities and first-order logical (FOL) queries in low-dimensional spaces -- has shown great power in multi-hop reasoning over knowledge graphs. Recently, embedding entities and queries with geometric shapes becomes a promising direction, as geometric shapes can naturally represent answer sets of queries and logical relationships among them. However, existing geometry-based models have difficulty in modeling queries with negation, which significantly limits their applicability. To address this challenge, we propose a novel query embedding model, namel…

Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service

Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models. Contrary to many existing open source tools, Comprehend Medical is scalable and does not require steep learning curve, dependencies, pipeline configurations, or installations. Currently, Comprehend Medical performs NER in five medical categories: Anatomy, Medical Condition, Medications, Protected Health Information (PHI) and …

Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant c…

Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation

In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use Natural Language Processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open…

使用实体抽取和神经重排序将患者有效匹配到临床试验

Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking

本文提出一种基于流水线的临床试验检索方法，通过数据丰富技术和两步训练的Transformer重排序方案，显著提升了患者与试验的匹配精度。

可靠提取临床随访指令：一种混合神经符号管道

Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline

提出混合神经符号管道，结合BioBERT实体提取和确定性日期算术，在合成门诊笔记上实现接近完美的(动作, 日期)对提取F1分数，优于直接生成方法。

临床文本中实体修饰符预测的迁移学习：应用于阿片类药物使用障碍病例检测

Transfer Learning for the Prediction of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection

本研究提出多任务Transformer架构联合预测临床实体修饰符，在SemEval 2015 Task 14上取得最优结果，并验证了迁移学习可有效应用于部分匹配的OUD数据集。