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Abstract
Representation of news events as latent feature vectors is essential for several tasks, such as news recommendation, news event linking, etc. However, representations proposed in the past fail to capture the complex network structure of news events. In this paper we propose Event2Vec, a novel way to learn latent feature vectors for news events using a network. We use recently proposed network embedding techniques, which are proven to be very effective for various prediction tasks in networks. As events involve different classes of nodes, such as named entities, temporal information, etc, general purpose network embeddings are agnostic to event semantics. To address this problem, we propose biased random walks that are tailored to capture the neighborhoods of news events in event networks. We then show that these learned embeddings are effective for news event recommendation and news event linking tasks using strong baselines, such as vanilla Node2Vec, and other state-of-the-art graph-based event ranking techniques.
Original language | English |
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Title of host publication | 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
Number of pages | 4 |
Publisher | Association for Computing Machinery |
Publication date | 27 Jun 2018 |
Pages | 1013-1016 |
ISBN (Electronic) | 9781450356572 |
DOIs | |
Publication status | Published - 27 Jun 2018 |
Event | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 - Ann Arbor, United States Duration: 8 Jul 2018 → 12 Jul 2018 |
Conference
Conference | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
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Country/Territory | United States |
City | Ann Arbor |
Period | 08/07/2018 → 12/07/2018 |
Sponsor | Special Interest Group on Information Retrieval (ACM SIGIR) |
Keywords
- Event2vec
- Network embeddings
- News classification
- News event embeddings
- News event representation
- News linking
- Node2vec
Fingerprint
Dive into the research topics of 'Event2Vec: Neural embeddings for news events'. Together they form a unique fingerprint.Projects
- 1 Finished
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QWeb: Querying the Web of Data Easily and Efficiently
Hose, K., Montoya, G., Prado, L. A. G. D., Rouces, J., Mathiassen, K. A. M., Zervakis, E. & Setty, V.
01/02/2015 → 31/10/2019
Project: Research