“意合图是一种以事件为中心的多层次语义表示方法,由事件结构与实体结构构成,通过多层次语义体系设计,实现对事件的多层次分析。本文细化并制定了意合图标注规范,采用分层分级的标注策略,在自主研发的在线标注系统中对新闻语料和国际中文教育阅读语料进行了意合图QNP标注工作。通过本次标注,验证了意合图体系的合理性和可标注性,并构建了意合图语义资源库。”

“基于参数的语义表示虽取得成就,但符号化的语义表示仍具有不可忽视的意义。我们在语义学基础上,充分考虑符号化语义表示在NLP领域落地中的需求,提出了一种兼具通用性与扩展性的多层次语义表示方法——意合图。意合图以事件为核心,由事件结构与实体结构构成,通过多层次语义体系设计,提升与场景结合的能力,并力求对不同层级的语言单元作一贯式表示。在资源建设和相关分析实验中取得良好效果。本文将重点介绍意合图设计理念与多层次语义体系。”

“中文意合图是近年提出的中文语义表示方法。本次评测是首次基于意合图理论的语义分析评测,旨在探索面向意合图理论的语义计算方法,评估机器的语义分析能力。本次评测共有14支队伍报名,最终有7支队伍提交结果,其中有5支队伍提交技术报告与模型,均成功复现。在评测截止时间内,表现最好的队伍使用大语言模型LoRA微调方法获得了F1值为72.06%的成绩。在最终提交技术报告的5支队伍中,有4支队伍使用了大语言模型微调方法,在一定程度上表明了目前技术发展的趋势。”

Although Dense Passage Retrieval (DPR) models have achieved significantly enhanced performance, their widespread application is still hindered by the demanding inference efficiency and high deployment costs. Knowledge distillation is an efficient method to compress models, which transfers knowledge from strong teacher models to weak student models. Previous studies have proved the effectiveness of knowledge distillation in DPR. However, there often remains a significant performance gap between the teacher and the distilled student. To narrow this performance gap, we propose MTA4DPR, a Multi-Teaching-Assistants based iterative knowledge distillation method for Dense Passage Retrieval, which transfers knowledge from the teacher to the student with the help of multiple assistants in an iterative manner; with each iteration, the student learns from more performant assistants and more difficult data. The experimental results show that our 66M student model achieves the state-of-the-art performance among models with same parameters on multiple datasets, and is very competitive when compared with larger, even LLM-based, DPR models.

一般情况下,一个小句中只有一个动词,但是也有两个动词同时在一个小句中出现的情况,比如两个动词接连出现在同一小句中,在句法上有可能构成状中、述补、动宾、连谓及并列等结构,语义上可能表示修饰、支配、并列等关系。连续使用的两个动词构成了相对复杂的结构与语义关系,尤其是在没有形式标记的情况下,如何自动识别连用动词的结构及其所表达的语义关系是句法语义分析在落地过程中面对的较为困难的问题。对此,本文将研究对象定位于直接作状语的动词,从大规模结构树库中抽取两个动词连用的情况,并对语料进行消歧,提取出作状语的动词后,进一步对其进行语义的细分类,最后构建相应的语义搭配库。不仅为语言学本体提供了分类参考,同时也为深层次的汉语句法语义分析提供了更多的知识。

以往的介词知识库构建重视介词语义和介宾的搭配研究,鲜有对介动搭配进行系统研究及知识获取的工作。而汉语介词发达及动词是句子中心的特征决定了介动搭配研究的重要性。本研究基于结构检索技术,充分借助短语结构属性和结构信息,从大规模语料中抽取介动搭配16033对。并提出了介动搭配紧密度的度量方法,初步分析证明其远优于依靠绝对频次进行搭配度量的方法。

基于词单元的经典依存语法在面向中文的句子分析中遇到诸多汉语特性引起的困难。为此,本文提出汉语的块依存语法,以谓词为核心,以组块为研究对象,在句内和句间寻找谓词所支配的组块,构建句群级别的句法分析框架。这一操作不仅仅是提升叶子节点的语言单位,而且还针对汉语语义特点进行了分析方式和分析规则上的创新,能够较好地解决微观层次的逻辑结构知识,并为中观论元知识和宏观篇章知识打好铺垫。本文主要介绍了块依存语法理念、表示、分析方法及特点,并简要介绍了块依存树库的构建情况。截至目前为止,树库规模为187万字符(超过4万复句、10万小句),其中包含67%新闻文本和32%百科文本。

This paper presents the NLPTEA 2018 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 20 teams registered for this shared task, 13 teams developed the system and submitted a total of 32 runs. Progress in system performances was obviously, reaching F1 of 36.12% in position level and 25.27% in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers.

This paper presents the IJCNLP 2017 shared task for Chinese grammatical error diagnosis (CGED) which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 13 teams registered for this shared task, 5 teams developed the system and submitted a total of 13 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.

This paper presents the NLP-TEA 2016 shared task for Chinese grammatical error diagnosis which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 15 teams registered for this shared task, 9 teams developed the system and submitted a total of 36 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.