Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional random field (CRF) sequence optimizer. These systems process the input sentence by sentence. This approach prevents the systems from capturing dependencies over sentence boundaries and makes accurate sentence boundary detection a prerequisite. Since sentence boundary detection can be problematic especially in clinical reports, where dependencies and co-references across sentence boundaries are abundant, these systems have clear limitations. In this study, we built a new system on the framework of one of the current state-of-the-art de-identification systems, NeuroNER, to overcome these limitations. This new system incorporates context embeddings through forward and backward n-grams without using sentence boundaries. Our context-enhanced de-identification (CEDI) system captures dependencies over sentence boundaries and bypasses the sentence boundary detection problem altogether. We enhanced this system with deep affix features and an attention mechanism to capture the pertinent parts of the input. The CEDI system outperforms NeuroNER on the 2006 i2b2 de-identification challenge dataset, the 2014 i2b2 shared task de-identification dataset, and the 2016 CEGS N-GRID de-identification dataset (p<0.01). All datasets comprise narrative clinical reports in English but contain different note types varying from discharge summaries to psychiatric notes. Enhancing CEDI with deep affix features and the attention mechanism further increased performance.
Scalar Adjective Identification and Multilingual Ranking
Université Paris-Saclay（巴黎-萨克雷大学）；CNRS, LISN（法国国家科学研究中心，LISN）；University of Helsinki（赫尔辛基大学）
标量形容词之间的强度关系（例如，nice < great < wonderful）与自然语言推理和常识推理高度相关。以往的标量形容词排名研究主要集中在英语，主要是由于可用的数据集进行评估。我们引入了一个新的多语种数据集，以促进新语言中标量形容词的研究。我们进行了一系列的实验，并在此数据集上设置性能基线，使用单语和多语种上下文语言模型。此外，我们引入了一个新的二元分类任务的英语标量形容词识别，检查模型的能力，区分标量关系形容词。我们探测情境化的表示，并报告基线结果，以便将来在这项任务上进行比较。
Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models
Research Laboratory in Algebra Numbers theory and Intelligent Systems（代数数论与智能系统研究实验室）；CNRS（法国国家科学研究中心）；Bordeaux INP（波尔多国立高等工程技术学院）；LaBRI（波尔多计算机科学实验室）；Laboratory LIG（LIG实验室）
