Event Causality Extraction (ECE) aims to extract causal event pairs from text. Existing methods overlook the interplay between causal event pairs and their corresponding textual evidence (e.g., causal event mention pairs), and fail to effectively leverage global causal dependency information. To address these issues, we propose a Mention-Level Causal Evidence and Global Causal Graph Reasoning (MLCE-GCGR) framework to enhance ECE. First, we introduce an auxiliary Event Mention Causality Extraction (EMCE) task, which extracts causal event mention pairs, to provide evidence for the main ECE task, and design a Dual-Level Interaction Enhancement (DLIE) strategy to enhance the bidirectional interplay between event-level and mention-level causality. Second, we develop a Global Causal Graph Reasoning (GCGR) module that simulates human-like multi-turn reasoning, aiming to progressively refine the causal graph by capturing global dependencies among event mentions, types, and arguments. Experiments on four benchmark datasets show that our method outperforms state-of-the-art approaches. Moreover, by extracting causal event mention pairs as supporting evidence, our approach improves the interpretability of structured causality extraction.

With the information overload in web services, micro-blog has been increasingly providing as a media for end-users to express their opinions. The notable feature of micro-blog articles is prone to be a burst of popularity during a short period. In addition, diverse interests make users bored in redundant items in most recommender systems. Therefore, providing users with diverse popular micro-blogs that suit their interesting topics is an important issue. In this paper, depending on forwarding number and comment number of micro-blogs, an effective model for popularity prediction is proposed to discover popular topics. Then, a MaxMin diversity algorithm based on content distance and popularity density is proposed to discover top k micro-blogs. Finally, we design a diverse personalized popularity attention (DPPA) recommendation approach for target user. We conduct extensive experiments on large scale micro-blog datasets. The experimental results show that our proposed approach can satisfy user's requirements with a higher recall than personal attention methods.