Search Results for author: Ru Li

Found 26 papers, 9 papers with code

基于框架语义映射和类型感知的篇章事件抽取(Document-Level Event Extraction Based on Frame Semantic Mapping and Type Awareness)

no code implementations CCL 2022 Jiang Lu, Ru Li, Xuefeng Su, Zhichao Yan, Jiaxing Chen

“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。”

Document-level Event Extraction Event Extraction

Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization

no code implementations EMNLP 2021 Yong Guan, Shaoru Guo, Ru Li, XiaoLi Li, Hongye Tan

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.

Extractive Summarization Extractive Text Summarization +1

基于Self-Attention的句法感知汉语框架语义角色标注(Syntax-Aware Chinese Frame Semantic Role Labeling Based on Self-Attention)

no code implementations CCL 2020 Xiaohui Wang, Ru Li, Zhiqiang Wang, Qinghua Chai, Xiaoqi Han

框架语义角色标注(Frame Semantic Role Labeling, FSRL)是基于FrameNet标注体系的语义分析任务。语义角色标注通常对句法有很强的依赖性, 目前的语义角色标注模型大多基于双向长短时记忆网络Bi-LSTM, 虽然可以获取句子中的长距离依赖信息, 但无法很好获取句子中的句法信息。因此, 引入self-attention机制来捕获句子中每个词的句法信息。实验结果表明, 该模型在CFN(Chinese FrameNet, 汉语框架网)数据集上的F1达到83. 77%, 提升了近11%。

Semantic Role Labeling

A Distance Metric Learning Model Based On Variational Information Bottleneck

no code implementations5 Mar 2024 YaoDan Zhang, Zidong Wang, Ru Jia, Ru Li

Compared with the general metric learning model MetricF, the prediction error is reduced by 7. 29%.

Metric Learning

Knowledge-Aware Neuron Interpretation for Scene Classification

no code implementations29 Jan 2024 Yong Guan, Freddy Lecue, Jiaoyan Chen, Ru Li, Jeff Z. Pan

Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts.

Classification Scene Classification

SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field

1 code implementation14 Dec 2023 Ru Li, Jia Liu, Guanghui Liu, Shengping Zhang, Bing Zeng, Shuaicheng Liu

We modify the classical spectral rendering into two main steps, 1) the generation of a series of spectrum maps spanning different wavelengths, 2) the combination of these spectrum maps for the RGB output.

Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta

no code implementations16 Nov 2023 Wei zhang, Dai Li, Chen Liang, Fang Zhou, Zhongke Zhang, Xuewei Wang, Ru Li, Yi Zhou, Yaning Huang, Dong Liang, Kai Wang, Zhangyuan Wang, Zhengxing Chen, Min Li, Fenggang Wu, Minghai Chen, Huayu Li, Yunnan Wu, Zhan Shu, Mindi Yuan, Sri Reddy

To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models.

Representation Learning

HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion

1 code implementation12 Aug 2023 Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan

Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers.

Attribute Relation

Rethinking Context Aggregation in Natural Image Matting

1 code implementation3 Apr 2023 Qinglin Liu, Shengping Zhang, Quanling Meng, Ru Li, Bineng Zhong, Liqiang Nie

For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background.

Image Matting

Transformer-based Entity Typing in Knowledge Graphs

1 code implementation20 Oct 2022 Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan

We investigate the knowledge graph entity typing task which aims at inferring plausible entity types.

Entity Typing Knowledge Graphs

Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs

1 code implementation2 May 2022 Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, XiaoLi Li, Ru Li, Jeff Z. Pan

Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information.

Knowledge Graphs Vocal Bursts Type Prediction

A Knowledge-Guided Framework for Frame Identification

no code implementations ACL 2021 Xuefeng Su, Ru Li, XiaoLi Li, Jeff Z. Pan, Hu Zhang, Qinghua Chai, Xiaoqi Han

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.

Semantic Parsing Sentence

JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks

no code implementations19 Jan 2021 Ru Li, Shuaicheng Liu, Guangfu Wang, Guanghui Liu, Bing Zeng

We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders.

Auxiliary Learning General Classification +2

Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension

no code implementations COLING 2020 Shaoru Guo, Yong Guan, Ru Li, XiaoLi Li, Hongye Tan

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.

Machine Reading Comprehension Natural Language Understanding

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