Projekter pr. år
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.
Originalsprog | Engelsk |
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Titel | 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
Antal sider | 4 |
Forlag | Association for Computing Machinery |
Publikationsdato | 27 jun. 2018 |
Sider | 1013-1016 |
ISBN (Elektronisk) | 9781450356572 |
DOI | |
Status | Udgivet - 27 jun. 2018 |
Begivenhed | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 - Ann Arbor, USA Varighed: 8 jul. 2018 → 12 jul. 2018 |
Konference
Konference | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
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Land/Område | USA |
By | Ann Arbor |
Periode | 08/07/2018 → 12/07/2018 |
Sponsor | Special Interest Group on Information Retrieval (ACM SIGIR) |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Event2Vec: Neural embeddings for news events'. Sammen danner de et unikt fingeraftryk.Projekter
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Projekter: Projekt › Forskning