12 accepted papers
Ontologies (schemas) are a key bottleneck for schema-grounded information extraction and knowledge graph construction, yet manual ontology engineering is expensive and schemas quickly fragment or drift across domains. We introduce SCOPE (Schema Construction and Ontology Induction Pipeline Evaluation…
Event schema generation is crucial for understanding the structure and temporal relationships of complex events. In this paper, we introduce a novel Directed Acyclic Graph Diffusion Model (DAGDM) that integrates DAG characteristics within a diffusion framework to enhance the effectiveness of schema…
Event Argument Extraction is a critical task of Event Extraction, focused on identifying event arguments within text. This paper presents a novel Fusion Selection-Generation-Based Approach, by combining the precision of selective methods with the semantic generation capability of generative methods…
Nüshu is an endangered language from Jiangyong County, China, and the world’s only known writing system created and used exclusively by women. Recent Natural Language Processing (NLP) work has digitized small Nüshu-Chinese corpora, but the script remains computationally inaccessible due to its handw
We would like to express our gratitude to the Reviewers and the Area Chair for their insightful comments and for recognizing the robustness of our proposed framework for analyzing the serial position effects (SPE) in LLMs. We appreciate the acknowledgment of our work in demonstrating the widespread…
Aspect-based summarization has seen significant advancements, especially in structured text. Yet, summarizing disordered, large-scale texts, like those found in social media and customer feedback, remains a significant challenge. Current research largely targets predefined aspects within structured…
The rapid proliferation of online content necessitates effective summarization methods, among which dynamic aspect-based summarization stands out. Unlike its traditional counterpart, which assumes a fixed set of known aspects, this approach adapts to the varied aspects of the input text. We introduc…
The ability for individuals to constructively engage with one another across lines of difference is a critical feature of a healthy pluralistic society. This is also true in online discussion spaces like social media platforms. To date, much social media research has focused on preventing ills—like…
Video summarization is an essential problem in signal processing, which intends to produce a concise summary of the original video. Existing video summarization approaches regard the task as a keyframe selection problem and generally construct the frame-wise representation by combining the long-rang…
Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important fi…
In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning. CACL consists of a 3D CNN and a video transformer which are used in parallel to generate diverse positive pairs for contrastive learning. This allows the model…
Knowledge graph embedding, which aims to learn representations of entities and relations in knowledge graphs, finds applications in various downstream tasks. The key to success of knowledge graph embedding models are the ability to model relation patterns including symmetry/antisymmetry, inversion,…