Shaoru Guo


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NutFrame: Frame-based Conceptual Structure Induction with LLMs
Shaoru Guo | Yubo Chen | Kang Liu | Ru Li | Jun Zhao
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Conceptual structure is fundamental to human cognition and natural language understanding. It is significant to explore whether Large Language Models (LLMs) understand such knowledge. Since FrameNet serves as a well-defined conceptual structure knowledge resource, with meaningful frames, fine-grained frame elements, and rich frame relations, we construct a benchmark for coNceptual structure induction based on FrameNet, called NutFrame. It contains three sub-tasks: Frame Induction, Frame Element Induction, and Frame Relation Induction. In addition, we utilize prompts to induce conceptual structure of Framenet with LLMs. Furthermore, we conduct extensive experiments on NutFrame to evaluate various widely-used LLMs. Experimental results demonstrate that FrameNet induction remains a challenge for LLMs.


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大模型与知识图谱(Large Language Models and Knowledge Graphs)
Yubo Chen (玉博 陈) | Shaoru Guo (少茹 郭) | Kang Liu (康 刘) | Jun Zhao (军 赵)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 2: Frontier Forum)


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EventOA: An Event Ontology Alignment Benchmark Based on FrameNet and Wikidata
Shaoru Guo | Chenhao Wang | Yubo Chen | Kang Liu | Ru Li | Jun Zhao
Findings of the Association for Computational Linguistics: ACL 2023

Event ontology provides a shared and formal specification about what happens in the real world and can benefit many natural language understanding tasks. However, the independent development of event ontologies often results in heterogeneous representations that raise the need for establishing alignments between semantically related events. There exists a series of works about ontology alignment (OA), but they only focus on the entity-based OA, and neglect the event-based OA. To fill the gap, we construct an Event Ontology Alignment (EventOA) dataset based on FrameNet and Wikidata, which consists of 900+ event type alignments and 8,000+ event argument alignments. Furthermore, we propose a multi-view event ontology alignment (MEOA) method, which utilizes description information (i.e., name, alias and definition) and neighbor information (i.e., subclass and superclass) to obtain richer representation of the event ontologies. Extensive experiments show that our MEOA outperforms the existing entity-based OA methods and can serve as a strong baseline for EventOA research.


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Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Yong Guan | Shaoru Guo | Ru Li | Xiaoli Li | Hu Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recently graph-based methods have been adopted for Abstractive Text Summarization. However, existing graph-based methods only consider either word relations or structure information, which neglect the correlation between them. To simultaneously capture the word relations and structure information from sentences, we propose a novel Dual Graph network for Abstractive Sentence Summarization. Specifically, we first construct semantic scenario graph and semantic word relation graph based on FrameNet, and subsequently learn their representations and design graph fusion method to enhance their correlation and obtain better semantic representation for summary generation. Experimental results show our model outperforms existing state-of-the-art methods on two popular benchmark datasets, i.e., Gigaword and DUC 2004.

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Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
Yong Guan | Shaoru Guo | Ru Li | Xiaoli Li | Hongye Tan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.


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A Frame-based Sentence Representation for Machine Reading Comprehension
Shaoru Guo | Ru Li | Hongye Tan | Xiaoli Li | Yong Guan | Hongyan Zhao | Yueping Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.

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Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
Shaoru Guo | Yong Guan | Ru Li | Xiaoli Li | Hongye Tan
Proceedings of the 28th International Conference on Computational Linguistics

Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding. It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature. This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation. Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.

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多模块联合的阅读理解候选句抽取(Evidence sentence extraction for reading comprehension based on multi-module)
Yu Ji (吉宇) | Xiaoyue Wang (王笑月) | Ru Li (李茹) | Shaoru Guo (郭少茹) | Yong Guan (关勇)
Proceedings of the 19th Chinese National Conference on Computational Linguistics