This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. Compared to most previous publicly available text understanding systems and tools, TexSmart holds some unique features. First, the NER function of TexSmart supports over 1,000 entity types, while most other public tools typically support several to (at most) dozens of entity types. Second, TexSmart introduces new semantic analysis functions like semantic expansion and deep semantic representation, that are absent in most previous systems. Third, a spectrum of algorithms (from very fast algorithms to those that are relatively slow but more accurate) are implemented for one function in TexSmart, to fulfill the requirements of different academic and industrial applications. The adoption of unsupervised or weakly-supervised algorithms is especially emphasized, with the goal of easily updating our models to include fresh data with less human annotation efforts.
The main contents of this report include major functions of TexSmart, algorithms for achieving these functions, how to use the TexSmart toolkit and Web APIs, and evaluation results of some key algorithms.
Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble
Huawei Cloud（华为云）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；Peking University（北京大学）；Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院，湖北省分布式系统安全重点实验室，湖北省大数据安全工程技术研究中心）
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University at Buffalo（布法罗大学）；Zhejiang Lab（之江实验室）；Tencent Medical AI Lab（腾讯医疗AI实验室）；Alibaba Group（阿里巴巴集团）；Tencent America（腾讯美国）
DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition
New York University Abu Dhabi（纽约大学阿布扎赫德分校）；Zhejiang University（浙江大学）；The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technology University（南阳技术大学）；University of Electronic Science and Technology of China（电子科技大学）；Texas A&M University（德克萨斯大学）；Squirrel AI
CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English
Michtom School of Computer Science, Brandeis University（布兰迪斯大学米克托姆计算机科学学院）；NLP & IR Group, School of Computer Science, UNED（西班牙国立远程教育大学计算机科学学院自然语言处理与信息检索组）
针对 CoNLL-03 English 测试集的标注缺陷与性能瓶颈，本文通过文档级细粒度错误分类分析高性能 NER 模型，并发布修正版 CoNLL# 以支持低噪声、可解释评估。
CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities
Universidade de Fortaleza（福塔莱萨大学）；Instituto Federal de Educação, Ciência e Tecnologia do Ceará（塞阿拉联邦教育、科学与技术学院）；Tribunal de Justiça do Estado do Ceará（塞阿拉州法院）
Coarse-to-Fine Pre-training for Named Entity Recognition
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Engineering, Westlake University（西湖大学工学院）；Xiaomi AI Lab, Xiaomi Inc.（小米人工智能实验室，小米公司）
Hierarchical Aligned Multimodal Learning for NER on Tweet Posts
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade Directories
CRH-EHESS（CRH-法国社会科学高等研究院）；EPITA Research & Development Laboratory (LRDE)（EPITA研究与开发实验室（LRDE））；LASTIG；Univ. Gustave Eiffel（古斯塔夫·埃菲尔大学）；IGN-ENSG（法国国家地理学院-国立地理科学学院）
DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers
Indian Institute of Technology Guwahati（印度理工学院古瓦哈蒂分校）
Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network
Shandong University（山东大学）；Alibaba Group（阿里巴巴集团）；Indiana University Bloomington（印第安纳大学伯明顿分校）；University of Amsterdam（阿姆斯特丹大学）；Ahold Delhaize Research（阿霍德德尔海兹研究院）
Code and Named Entity Recognition in StackOverflow
The Ohio State University（俄亥俄州立大学）
随着大量的编程文本在互联网上变得容易获得，一起学习自然语言和计算机代码的兴趣越来越大。例如，StackOverflow目前有超过1500万个编程相关的问题，由850万用户编写。与此同时，仍然缺乏基本的NLP技术来识别出现在自然语言句子中的代码令牌或软件相关的命名实体。在本文中，我们介绍了一个新的命名实体识别（NER）语料库的计算机编程领域，由15,372个句子与20个细粒度的实体类型注释。我们在来自StackOverflow的1.52亿个句子上训练了域内BERT表示（BERTOverflow），这导致了比现成的BERT绝对增加了+10的F-1分数。我们还提出了SoftNER模型，该模型在StackOverflow数据上实现了代码和命名实体识别的总体79.10 F $_1$得分。我们的SoftNER模型采用了一个上下文无关的代码令牌分类器与语料库级别的功能，以改善基于BERT的标记模型。我们的代码和数据可在https://github.com/jeniyat/StackOverflowNER/上获得
