Abstract
Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. 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 event-driven stock prediction. On the other hand, events extracted from raw texts do not contain background knowledge on entities and relations that they are mentioned. To address this issue, this paper proposes to leverage extra information from knowledge graph, which provides ground truth such as attributes and properties of entities and encodes valuable relations between entities. Specifically, we propose a joint model to combine knowledge graph information into the objective function of an event embedding learning model. Experiments on event similarity and stock market prediction show that our model is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities.- Anthology ID:
- C16-1201
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2133–2142
- Language:
- URL:
- https://aclanthology.org/C16-1201
- DOI:
- Cite (ACL):
- Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2016. Knowledge-Driven Event Embedding for Stock Prediction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2133–2142, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Knowledge-Driven Event Embedding for Stock Prediction (Ding et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/C16-1201.pdf
- Data
- YAGO