Generating Fine-Grained Open Vocabulary Entity Type Descriptions
| Autor: | Gerard de Melo, Rajarshi Bhowmik |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Sequence Vocabulary Computer Science - Computation and Language Computer science business.industry media_common.quotation_subject Context (language use) 02 engineering and technology 010501 environmental sciences Type (model theory) computer.software_genre 01 natural sciences Entity type 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Computation and Language (cs.CL) computer Natural language processing 0105 earth and related environmental sciences media_common |
| Zdroj: | ACL (1) |
| DOI: | 10.18653/v1/p18-1081 |
| Popis: | While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graph entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines. Published in ACL 2018 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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