Yansong Feng

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

2019 · arXiv (Cornell University)

Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we …

Jointly Learning Entity and Relation Representations for Entity Alignment

2019

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding …

Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

2015

Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models directly work on raw word sequences or constituent parse trees, thus often suffer from irrelevant information introduced when subjects …

How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models

2017

Generative conversational systems are attracting increasing attention in natural language processing (NLP). Recently, researchers have noticed the importance of context information in dialog processing, and built various models to utilize context. However, there is no …

Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems

2017

The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq …

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

2018 · arXiv (Cornell University)

The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant …

Question Answering on Freebase via Relation Extraction and Textual Evidence

2016

Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic …