1.4k citations · 5.4k across the 120 of their papers we have counts for
19 papers · 1 filter
Knowledge Representation Learning: A Quantitative Review
Yankai Lin, Xu Han, Ruobing Xie +2
Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowle…
COSINE: Compressive Network Embedding on Large-scale Information Networks
Zhengyan Zhang, Cheng Yang, Zhiyuan Liu +4
There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. As there are many large-scale real-world networks, it's inefficient for existing…
Neural Diffusion Model for Microscopic Cascade Prediction
Cheng Yang, Maosong Sun, Haoran Liu +3
The prediction of information diffusion or cascade has attracted much attention over the last decade. Most cascade prediction works target on predicting cascade-level macroscopic p…
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Shengding Hu +6
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predictin…
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
Yixin Cao, Lei Hou, Juanzi Li +4
Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for…
Neural Collective Entity Linking
Yixin Cao, Lei Hou, Juanzi Li +1
Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local c…
Learning to Embed Sentences Using Attentive Recursive Trees
Jiaxin Shi, Lei Hou, Juanzi Li +2
Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks…
Differentiating Concepts and Instances for Knowledge Graph Embedding
Xin Lv, Lei Hou, Juanzi Li +1
Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding met…
CED: Credible Early Detection of Social Media Rumors
Changhe Song, Cunchao Tu, Cheng Yang +2
Rumors spread dramatically fast through online social media services, and people are exploring methods to detect rumors automatically. Existing methods typically learn semantic rep…
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
Jiaxin Shi, Chen Liang, Lei Hou +3
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience scor…
Language Modeling with Sparse Product of Sememe Experts
Yihong Gu, Jun Yan, Hao Zhu +5
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but no…
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
Xu Han, Hao Zhu, Pengfei Yu +4
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation…
Overview of CAIL2018: Legal Judgment Prediction Competition
Haoxi Zhong, Chaojun Xiao, Zhipeng Guo +8
In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to pred…
Consistency and Variation in Kernel Neural Ranking Model
Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi +4
This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deplo…
Automatic Judgment Prediction via Legal Reading Comprehension
Shangbang Long, Cunchao Tu, Zhiyuan Liu +1
Automatic judgment prediction aims to predict the judicial results based on case materials. It has been studied for several decades mainly by lawyers and judges, considered as a no…
CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction
Chaojun Xiao, Haoxi Zhong, Zhipeng Guo +8
In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. \da…
Incorporating Chinese Characters of Words for Lexical Sememe Prediction
Huiming Jin, Hao Zhu, Zhiyuan Liu +4
Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their…
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
Zhenghao Liu, Chenyan Xiong, Maosong Sun +1
This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words an…
Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training
Xu Han, Zhiyuan Liu, Maosong Sun
Existing neural relation extraction (NRE) models rely on distant supervision and suffer from wrong labeling problems. In this paper, we propose a novel adversarial training mechani…