CCL 23-Eval任务4系统报告:基于深度学习的空间语义理解(System Report for CCL 23-Eval Task4:Spatial Semantic Understanding Based on Deep Learning.) (2023)

Abstract: “本文介绍了参赛系统在第三届中文空间语义理解评测(SpaCE2023)采用的技术路线:面向空间语义异常识别任务提出了抽取方法,并结合生成器进一步完成了空间语义角色标注任务,空间场景异同判断任务则使用了大语言模型生成。本文进一步探索了大语言模型在评测数据集上的应用,发现指令设计是未来工作的重点和难点。参赛系统的代码和模型见https://github.com/ShacklesLay/Space2023。”

## introduction chinese frame semantic parsing (cfsp) is a semantic parsing task based on chinese framenet (cfn) @xcite . to gain a thorough understanding of the events included in the sentence, it aims to extract the frame semantic structure from the sentence, including identifying the frame activated by the target word, frame elements, etc. in downstream tasks like relation extraction @xcite and reading comprehension @xcite , fsp is extremely important. ## related work the framenet is a taxonomy of manually identified semantic frames for english @xcite . listed in the fn with each frame are a set of lemmas with part of speech that can evoke the frame, which are called lexical units (lus). accompanying most lus in the fn is a set of exemples annotated for them. moreover, there are a set of labeled relations between frames @xcite . frame identification can be abstracted as an event extraction task. certain approaches employ pattern matching techniques by learning patterns from annotated corpora to extract information from documents @xcite . subsequently, machine learning-based event extraction treats event category classification and event element extraction as classification problems, aiming to categorize event-triggering words into their respective event types @xcite @xcite . in recent years, the remarkable feature representation capabilities demonstrated by deep learning methods @xcite @xcite have yielded impressive results in event extraction. by fine-tuning pretrained models on downstream tasks @xcite , these methods have significantly enhanced overall performance. since event detection benefits many nlp applications @xcite , intensive efforts have been devoted to identifying their arguments. several studies use dependency parsers to obtain features @xcite , or use sequence labeling as a viable solution @xcite @xcite . meanwhile, argument identification has been recently addressed by end-to-end event extraction models, @xcite @xcite . lately, some works reformulated the task as a question answering problem @xcite @xcite )or as a constrained text generation problem @xcite using predefined prompts or templates. role classification is a subtask of argument extraction, the goal is to identify the semantic role assumed by argument. researchers usually model this task as a text classification problem. meanwhile, using the interaction between argument roles can improve the performance of argument extraction @xcite . framenet is a typical method for frames emantic parsing. it consists of mapping a predicate into a frame, and analysis of the frame's elements. framenet has been applied to many downstream tasks, such as machine reading comprehension and text summarization @xcite . chinese framenet is a frame semantic resource refer to framenet, and based on chinese corpus, including frames, frame elements, lexical units and frame relations. chinese framenet is increasingly important in chinese information processing. ## future work due to limited evaluation time, we have some unfinished attempts on the three sub-tasks.