Detection and disambiguation of all entities in text is a crucial task for a wide range of applications. The typical formulation of the problem involves two stages: detect mention boundaries and link all mentions to a knowledge base. For a long time, mention detection has been considered as a necessary step for extracting all entities in a piece of text, even if the information about mention spans is ignored by some downstream applications that merely focus on the set of extracted entities. In this paper we show that, in such cases, detection of mention boundaries does not bring any considerable performance gain in extracting entities, and therefore can be skipped. To conduct our analysis, we propose an "Entity Tagging" formulation of the problem, where models are evaluated purely on the set of extracted entities without considering mentions. We compare a state-of-the-art mention-aware entity linking solution against GET, a mention-agnostic sequence-to-sequence model that simply outputs a list of disambiguated entities given an input context. We find that these models achieve comparable performance when trained both on a fully and partially annotated dataset across multiple benchmarks, demonstrating that GET can extract disambiguated entities with strong performance without explicit mention boundaries supervision.
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
Korea University（高丽大学）；University of California, San Diego（加州大学圣迭戈分校）
本文是关于我们的系统提交给BioCreative VII Track 2挑战的化学鉴别任务的技术报告。这一挑战的主要特点是数据由全文文章组成，而当前的数据集通常只包括标题和摘要。为了有效地解决这个问题，我们的目标是提高标签的一致性和实体覆盖率，使用各种方法，如在相同的文章中的多数表决命名实体识别（NER）和一个混合的方法，结合字典和神经模型进行规范化。在NLM-Chem数据集上的实验中，我们表明我们的方法提高了模型的性能，特别是在召回方面。最后，在挑战赛的官方评估中，我们的系统在NER中排名第一，表现明显优于基线模型和来自16个团队的80多个提交。
PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
INESC TEC；FCUP（波尔图大学理学院）；ICMC-USP（圣保罗大学圣卡洛斯数学与计算机科学研究所）；University of São Paulo（圣保罗大学）；FEP（波尔图大学经济学院）
本文论述了实体提取任务（命名实体识别）的文本挖掘过程，旨在揭示非平凡的语义结构，如实体或社区之间的关系和相互作用。本文提出了一种简单有效的命名实体抽取算法。该方法采用灵活的模式匹配、词性标注和基于词典的规则，称为PAMPO（Pattern Matching and PO s tagingbased algorithm for NER）。它被开发用于处理用葡萄牙语编写的文本，但它也可能适用于其他语言。我们比较我们的方法与目前的替代品，支持命名实体识别（NER）的内容写在葡萄牙语。这些是炼金术师泽曼塔和伦勃朗在葡萄牙语写的几个文本上的实体提取方法的疗效评价表明，$recall$和$F_1$措施有相当大的改善。
Resource Mention Extraction for MOOC Discussion Forums
University of Electronic Science and Technology of China（电子科技大学）；National University of Singapore（新加坡国立大学）；NUS Graduate School for Integrative Sciences and Engineering（新加坡国立大学综合科学与工程研究生院）；NUS Institute for Application of Learning Science and Educational Technology（新加坡国立大学学习科学与教育技术应用研究所）
Parts-of-Speech Tagger Errors Do Not Necessarily Degrade Accuracy in Extracting Information from Biomedical Text
CRC for Innovative Dairy Products（创新乳制品合作研究中心）；The University of Melbourne（墨尔本大学）；Victorian Bioinformatics Consortium（维多利亚生物信息学联盟）；Monash University（莫纳什大学）
