Abstract
We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich
Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.
Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.
| Original language | English |
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| Pages | 324-329 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 30 Jul 2017 |
| Event | 55th Annual Meeting of the Association for Computational Linguistics - Vancouver, Canada Duration: 30 Jul 2017 → 4 Aug 2017 |
Conference
| Conference | 55th Annual Meeting of the Association for Computational Linguistics |
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| Country/Territory | Canada |
| City | Vancouver |
| Period | 30/07/17 → 4/08/17 |