109 Result(s)
within
Yi**ang Chen
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Storage and Retrieval
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Chapter and Conference Paper
Artificial Intelligences on Automated Context-Brain Recognition with Mobile Detection Devices
In the past few decades, lots of studies were proposed on investigations of brain circuits for physical health, mental health, educational learning, controlling system and so on. However, very few studies conc...
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Chapter and Conference Paper
NTDA: Noise-Tolerant Data Augmentation for Document-Level Event Argument Extraction
Event argument extraction (EAE), aiming at identifying event arguments over multiple sentences, mainly faces data sparsity problem. Cross-domain data augmentation can leverage annotated data to augment trainin...
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Chapter
Activity Recognition
This chapter presents an overview of human activity recognition (HAR). In ubiquitous computing, it is of extreme importance to sense and understand human’s behaviors and activities, which can then be used as i...
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Chapter and Conference Paper
Effects of Fairness and Explanation on Trust in Ethical AI
AI ethics has been a much discussed topic in recent years. Fairness and explainability are two important ethical principles for trustworthy AI. In this paper, the impact of AI explainability and fairness on us...
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Chapter and Conference Paper
Incorporating Ranking Context for End-to-End BERT Re-ranking
Ranking context has been shown crucial for the performance of learning to rank. Its use for the BERT-based re-rankers, however, has not been fully explored. In this work, an end-to-end BERT-based ranking model...
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Chapter and Conference Paper
A Two-Stage Self-adaptive Model for Passenger Flow Prediction on Schedule-Based Railway System
Platform-level passenger flow prediction is crucial for addressing the overcrowding problem on platforms that endangered the passengers’ safety and experience in railway systems. Although some studies exist on...
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Chapter and Conference Paper
Guwen-UNILM: Machine Translation Between Ancient and Modern Chinese Based on Pre-Trained Models
Ancient Chinese literatures are not only the unique cultural heritage of China but also the treasures of world civilization. Nevertheless, it has become quite difficult for modern people to comprehend or even ...
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Chapter and Conference Paper
Emotion Classification with Explicit and Implicit Syntactic Information
Emotion classification has become a hot research topic in natural language processing due to its wide application. Existing studies suffer from the error propagation problem when using the syntax information i...
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Chapter and Conference Paper
On Robustness and Bias Analysis of BERT-Based Relation Extraction
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains poorly understood. We do not know, ...
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Chapter and Conference Paper
ISWR: An Implicit Sentiment Words Recognition Model Based on Sentiment Propagation
Considering that there have no efficient methods to detect implicit sentiment words, this paper proposes an implicit sentiment words recognition model based on sentiment propagation to automatically identify i...
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Chapter and Conference Paper
Towards Nested and Fine-Grained Open Information Extraction
Open Information Extraction is a crucial task in natural language processing with wide applications. Existing efforts only work on extracting simple flat triplets that are not minimized, which neglect triplets...
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Chapter and Conference Paper
Document-Level Relation Extraction with Entity Enhancement and Context Refinement
Document-level Relation Extraction (DocRE) is the task of extracting relational facts mentioned in the entire document. Despite its popularity, there are still two major difficulties with this task: (i) How to...
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Chapter and Conference Paper
Graph Attention Collaborative Similarity Embedding for Recommender System
We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning. Our f...
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Chapter and Conference Paper
Improving Entity Linking with Graph Networks
Entity linking aims to assign a unique identity to entities mentioned in text given a predefined Knowledge Base. Previous works address this task based on the local or global features or the combination of the...
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Chapter and Conference Paper
Distributed Differential Evolution for Anonymity-Driven Vertical Fragmentation in Outsourced Data Storage
Vertical fragmentation is a promising technique for outsourced data storage. It can protect data privacy while conserving original data without any transformation. Previous vertical fragmentation approaches ne...
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Chapter and Conference Paper
A Novel Repair-Based Multi-objective Algorithm for QoS-Constrained Distributed Data-Intensive Web Service Composition
Distributed Data-intensive Web service composition (DWSC) implements new applications by using existing Web services distributed over the Internet. DWSC has multiple conflicting objectives, e.g. minimising respon...
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Article
Model-free inference of diffusion networks using RKHS embeddings
We revisit in this paper the problem of inferring a diffusion network from information cascades. In our study, we make no assumptions on the underlying diffusion model, in this way obtaining a generic method w...
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Chapter and Conference Paper
WebEL: Improving Entity Linking with Extra Web Contexts
Entity Linking is the task of determining the identity of textual entity mentions given a predefined Knowledge Graph (KG). Plenty of existing efforts have been made on this task using either “local” informatio...
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Chapter and Conference Paper
Multiple Interaction Attention Model for Open-World Knowledge Graph Completion
Knowledge Graph Completion (KGC) aims at complementing missing relationships between entities in a Knowledge Graph (KG). While closed-world KGC approaches utilizing the knowledge within KG could only complemen...
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Chapter and Conference Paper
Unsupervised Entity Alignment Using Attribute Triples and Relation Triples
Entity alignment aims to find entities referring to the same real-world object across different knowledge graphs (KGs). Most existing works utilize the relations between entities contained in the relation trip...
