Mathematics is a highly specialized domain with its own unique set of challenges. Despite this, there has been relatively little research on natural language processing for mathematical texts, and there are few mathematical language resources aimed at NLP. In this paper, we aim to provide annotated corpora that can be used to study the language of mathematics in different contexts, ranging from fundamental concepts found in textbooks to advanced research mathematics. We preprocess the corpora with a neural parsing model and some manual intervention to provide part-of-speech tags, lemmas, and dependency trees. In total, we provide 182397 sentences across three corpora. We then aim to test and evaluate several noteworthy natural language processing models using these corpora, to show how well they can adapt to the domain of mathematics and provide useful tools for exploring mathematical language. We evaluate several neural and symbolic models against benchmarks that we extract from the corpus metadata to show that terminology extraction and definition extraction do not easily generalize to mathematics, and that additional work is needed to achieve good performance on these metrics. Finally, we provide a learning assistant that grants access to the content of these corpora in a context-sensitive manner, utilizing text search and entity linking. Though our corpora and benchmarks provide useful metrics for evaluating mathematical language processing, further work is necessary to adapt models to mathematics in order to provide more effective learning assistants and apply NLP methods to different mathematical domains.
Riveter: Measuring Power and Social Dynamics Between Entities
Allen Institute for AI（艾伦人工智能研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；Emory University（埃默里大学）
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages
BIFOLD；TU Berlin（柏林工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Université Paris-Saclay（巴黎萨克雷大学）；Nara Institute of Science and Technology（奈良科学技术研究所）；RIKEN（理化学研究所）
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；WeChat AI（微信人工智能）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学智能产业研究院（AIR））；International Innovation Center of Tsinghua University（清华大学国际创新中心）；Jiangsu Collaborative Innovation Center for Language Ability（江苏语言能力协同创新中心）；Institute Guo Qiang, Tsinghua University（清华大学国强研究院）
Explainable Natural Language Processing for Corporate Sustainability Analysis
College of Design and Engineering, National University of Singapore（新加坡国立大学设计与工程学院）；Asian Institute of Digital Finance, National University of Singapore（新加坡国立大学亚洲数字金融研究所）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科学技术研究局高性能计算研究所）；Sustainable and Green Finance Institute, National University of Singapore（新加坡国立大学可持续与绿色金融研究所）；NUS Business School, National University of Singapore（新加坡国立大学商学院）
Korean Named Entity Recognition Based on Language-Specific Features
The University of British Columbia（不列颠哥伦比亚大学）；The Chinese University of Hong Kong（香港中文大学）；Seoul National University of Science and Technology（首尔科学技术大学）
Automated Annotation of Evolving Corpora for Augmenting Longitudinal Network Data: A Framework Integrating Large Language Models and Expert Knowledge
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of International Studies and Institute for Carbon Neutrality, Peking University（北京大学国际关系学院与碳中和研究院）；Analytics Lab for Global Risk Politics, Peking University（北京大学全球风险政治分析实验室）
An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets
NRC "Kurchatov institute"（国家研究中心“库尔恰托夫研究所”）；MEPhI National Research Nuclear University（莫斯科工程物理学院国家核研究大学）；I.M. Sechenov First Moscow State Medical University (Sechenov University)（谢切诺夫第一莫斯科国立医科大学（谢切诺夫大学））
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Cyberspace Security, Southeast University（东南大学网络空间安全学院）
A Unified Biomedical Named Entity Recognition Framework with Large Language Models
College of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Air Force Communications NCO Academy（空军通信NCO学院）；Department of Pharmacy, Second Affiliated Hospital of Dalian Medical University（大连医科大学第二附属医院药学部）
