Inspired by early research on exploring naturally annotated data for Chinese Word Segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to explicitly mine word boundaries from speech-text parallel data. We employ the Montreal Forced Aligner (MFA) toolkit to perform character-level alignment on speech-text data, giving pauses as candidate word boundaries. Based on detailed analysis of collected pauses, we propose an effective probability-based strategy for filtering unreliable word boundaries. To more effectively utilize word boundaries as extra training data, we also propose a robust complete-then-train (CTT) strategy. We conduct cross-domain CWS experiments on two target domains, i.e., ZX and AISHELL2. We have annotated about 1,000 sentences as the evaluation data of AISHELL2. Experiments demonstrate the effectiveness of our proposed approach.
Parsing Through Boundaries in Chinese Word Segmentation
The Chinese University of Hong Kong, Hong Kong（香港中文大学）；University College London, United Kingdom（伦敦大学学院）；The University of British Columbia, Canada（不列颠哥伦比亚大学）
MarkBERT: Marking Word Boundaries Improves Chinese BERT
Tencent AI Lab（腾讯人工智能实验室）
我们提出了一个中文的BERT模型称为马克BERT，在这项工作中使用的词信息。现有的基于词的BERT模型以词为基本单位，但由于BERT的词汇量限制，只能覆盖高频词，遇到词汇表外（out-of-vocabulary，OOV）词时又退回到字符级。与现有的作品不同，MarkBERT保持词汇是汉字，并在相邻的单词之间插入边界标记。这种设计使得模型能够以相同的方式处理任何单词，无论它们是否是OOV单词。此外，我们的模型还有两个额外的好处：第一，它可以方便地在标记上添加单词级学习目标，这是对传统字符和单词级预训练任务的补充;第二，它可以通过用特定于POS标签的标记替换通用标记来轻松地整合更丰富的语义，例如单词的POS标签。通过简单的标记插入，MarkBERT可以提高各种下游任务的性能，包括语言理解和序列标记。\footnote{All the codes and models will be made publicly available at \url{https://github.com/daiyongya/markbert}}
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs
East China University of Science and Technology（华东理工大学）；University of Shanghai for Science and Technology（上海理工大学）
医院信息系统中的电子健康档案（Electronic Health Records，EHR）包含了患者的诊断和治疗信息，因此EHR对临床数据采掘至关重要。中文分词（CWS）是挖掘过程中的一个基本而重要的任务，现有的分词方法大多依赖于大规模的人工标注数据。由于注释是耗时且昂贵的，因此已经致力于诸如主动学习的技术来定位用于建模的信息量最大的样本。本文顺应这一趋势，提出了一种针对EHR中CWS的主动学习方法。具体地说，提出了一种结合归一化熵和损失预测（NE-LP）的采样策略来选择最具代表性的数据。同时，为了最小化学习的计算成本，我们提出了一个联合模型，包括一个词分割器和一个损失预测模型。此外，为了捕捉相邻字符之间的交互，在联合模型中还应用了二元组特征。为了说明NE-LP的有效性，我们对上海中医药大学附属曙光医院的EHR进行了实验。结果表明，NE-LP始终优于传统的基于不确定性的采样策略的主动学习在CWS。
Advancing Chinese biomedical text mining with community challenges
Sichuan University（四川大学）；West China Hospital（华西医院）；Tongji University（同济大学）；Shanghai Business School（上海商学院）；Takeda Co. Ltd.（武田制药有限公司）；Harbin Institute of Technology（哈尔滨工业大学）
Online shopping behavior study based on multi-granularity opinion mining: China vs. America
Nanjing University of Science and Technology（南京理工大学）；Alibaba Research Center for Complex Sciences, Hangzhou Normal University（杭州师范大学阿里巴巴复杂科学研究中心）；Hangzhou Normal University（杭州师范大学）；Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing University（南京大学江苏省数据工程与知识服务重点实验室）；Nanjing University（南京大学）
MITRA: A Large-Scale Parallel Corpus and Multilingual Pretrained Language Model for Machine Translation and Semantic Retrieval for Pāli, Sanskrit, Buddhist Chinese, and Tibetan
Center for Integrated Japanese Studies, Tohoku University（东京东京大学综合日语研究机构）；University of California, Berkeley（加州大学伯克利分校）；Berkeley Artificial Intelligence Research (BAIR)（伯克利人工智能研究（BAIR））
A Data Synthesis Method Driven by Large Language Models for Proactive Mining of Implicit User Intentions in Tourism
School of Computer & Communication Engineering, University of Science and Technology Beijing（信息与通信工程学院，北京科技大学）；School of Computer Science, University of South China（计算机科学学院，南方大学）；Department of Computer Science, Blekinge institute of Technology（计算机科学系，布莱京厄学院）
ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data
Beijing Jiaotong University（北京交通大学）；Hubei Provincial Hospital of Traditional Chinese Medicine（湖北省中医院）；China Academy of Chinese Medical Sciences（中国中医科学院）；Hubei University of Chinese Medicine（湖北中医药大学）；Guang'anmen Hospital, China Academy of Chinese Medical Sciences（中国中医科学院广安门医院）；Zhejiang University（浙江大学）
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
School of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）；School of Computer Science and Cyberspace Security, Hainan University（海南大学计算机与网络空间安全学院）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Defense Technology（国防科技大学）；Department of Mathematics and Computer Science, Northeastern State University（东北州立大学数学与计算机科学系）
Are you going to finish that? A Practical Study of the Partial Token Problem
Paul G. Allen School of Computer Science（保罗·G·艾伦计算机科学与工程学院）；Engineering, University of Washington（华盛顿大学工程学院）；Nvidia（Nvidia公司）；Allen Institute for AI（艾伦人工智能研究所）
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Honor Device Co., Ltd（荣誉设备有限公司）；Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究所）；Pengcheng Laboratory（鹏城实验室）；Research Institute of Intelligent Complex Systems, Fudan University（复旦大学智能复杂系统研究院）；Shanghai Key Laboratory of Intelligent Information Processing（上海智能信息处理重点实验室）
