Event Representation Learning Enhanced with External Commonsense Knowledge

1 code implementation • IJCNLP 2019 • Xiao Ding, Kuo Liao, Ting Liu, Zhongyang Li, Junwen Duan

Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction.

Representation Learning Stock Market Prediction

Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction

1 code implementation • COLING 2018 • Junwen Duan, Yue Zhang, Xiao Ding, Ching-Yun Chang, Ting Liu

The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling.

Information Retrieval Stock Market Prediction

Learning Sentence Representations over Tree Structures for Target-Dependent Classification

no code implementations • NAACL 2018 • Junwen Duan, Xiao Ding, Ting Liu

To address above issues, we propose a reinforcement learning based approach, which automatically induces target-specific sentence representations over tree structures.

Classification General Classification +3

Knowledge-Driven Event Embedding for Stock Prediction

no code implementations • COLING 2016 • Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan

Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications.

Information Retrieval Open Information Extraction +4

Using Structured Events to Predict Stock Price Movement: An Empirical Investigation

no code implementations • EMNLP 2014 • Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan

Information Retrieval Open Information Extraction +1