“汉语中普遍存在一些语义场景,其语义核心不是以单个词语呈现,而是通过句子中的某个特定结构来表达。然而当前公开发表的数据集中,只有极少数的数据集将这种特定结构作为语义单元进行研究。汉语框架语义知识库是进行汉语深层语义分析与推理的优质资源,目前其激活框架的基本单位均为句中的一个词。本文以汉语框架语义知识库为基础,引入构式语法,使用2020《人民日报》语料库,以“以A为B”构式为例,建立了基于“以A为B”构式的汉语框架识别数据集,包含23849条例句,相应框架141个。本文使用多个汉语框架识别模型及大语言模型在该数据集上进行了实验,并针对传统框架识别模型在以构式为目标词的框架识别任务中由于目标词信息匮乏导致的识别困难问题,提出了基于目标词转化和数据增强的两种方法,使模型准确率达到了88.19%,有效提升了模型挖掘构式蕴含的深层语义信息的能力。”

“Chinese Frame-semantic Parsing (CFSP) aims to extract fine-grained frame-semantic structures from texts, which can provide fine-grained semantic information for natural language understanding models to enhance their abilities of semantic representations. Based on the CCL-23 CFSP evaluation task, we introduce construction grammar to expand the targets, as basic units activating frames in texts, from word-style to construction-style, and publish a more challenging CFSP evaluation task in CCL-2024. The evaluation dataset consists of 22,000 annotated examples involving nearly 695 frames. The evaluation task is divided into three subtasks: frame identification, argument identification, and role identification, involving two tracks: close track and open track. The evaluation task has attracted wide attention from both industry and academia, with a total of 1988 participating teams. As for the evaluation results, the team from China University of Petroleum won the first place in the closed track with the final score of 71.34, while the team frome Suzhou University won the first place in the open track with the final socre of 48.77. In this article, we reports the key information about the evaluation task, including key concepts, evaluation dataset, top-3 results and corresponding methods. More information about this task can be found on the website of the CCL-2024 CFSP evaluation task.”

“零形式填充是在篇章上下文中为给定句子中的隐式框架语义角色找到相应的填充内容。传统的零形式填充方法采用pipeline模型,容易造成错误传播,并且忽略了显式语义角色及其填充内容的重要性。针对上述问题,本文提出了一种端到端的零形式填充方法,该方法结合汉语框架网信息构建出框架语义场景图并利用GAT对其建模,得到融合了显式框架元素信息的候选填充项表示,增强了模型对句中隐式语义成分的识别能力。在汉语零形式填充数据集上的实验表明,本文提出的模型相较于基于Bert的基线模型F1值提升了9.16%,证明了本文提出方法的有效性。”

“汉语框架语义解析评测任务致力于提升机器模型理解细粒度语义信息的能力。该评测数据集包括20000条标注的框架语义解析例句和近700个框架信息。评测任务分为框架识别、论元范围识别和论元角色识别三个子任务,最终成绩根据这三个任务的得分综合计算。本次评测受到工业界和学术界的广泛关注,共有55支队伍报名参赛,其中12支队伍提交了结果,我们选取5支队伍的模型进行结果复现,最终来自四川的李作恒以71.49的分数排名第一。该任务的更多信息,包括系统提交、评测结果以及数据资源,可从CCL-2023汉语框架语义解析评测任务网址1查看。”

“汉语框架排歧旨在候选框架中给句子中的目标词选择一个符合其语义场景的框架。目前研究方法存在隐层向量的计算与目标词无关,并且忽略了句法结构信息对框架排歧的影响等缺陷。针对上述问题,使用GCN对句法结构信息进行建模;引入门机制过滤隐层向量中与目标词无关的噪声信息;并在此基础上,提出一种约束机制来约束模型的学习,改进向量表示。该模型在CFN、FN1.5和FN1.7数据集上优于当前最好模型,证明了方法的有效性。”

“篇章事件抽取是从给定的文本中识别其事件类型和事件论元。目前篇章事件普遍存在数据稀疏和多值论元耦合的问题。基于此,本文将汉语框架网(CFN)与中文篇章事件建立映射,同时引入滑窗机制和触发词释义改善了事件检测的数据稀疏问题;使用基于类型感知标签的多事件分离策略缓解了论元耦合问题。为了提升模型的鲁棒性,进一步引入对抗训练。本文提出的方法在DuEE-Fin和CCKS2021数据集上实验结果显著优于现有方法。”

Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KGFI significantly outperforms the state-of-the-art methods on two benchmark datasets.