Conference proceeding
Exploring Sentence Embedding Structures for Semantic Relation Extraction
2021 International Joint Conference on Neural Networks (IJCNN), v 2021-
18 Jul 2021
Abstract
Sentence embeddings encode natural language sentences as low-dimensional, dense vectors and have improved NLP tasks, including relation extraction, which aims at identifying structured relations defined in a knowledge base from unstructured text. A promising and more efficient approach would be to embed both the text and structured knowledge in low-dimensional spaces and discover alignments between them. We develop such an alignment procedure and evaluate the extent to which sentences carrying similar senses are embedded in close proximity sub-spaces, using that structure to align them to a knowledge graph. Our experimental results show that embedding spaces generated from simple models outperform those from more complicated approaches for the alignment and relation extraction task. We also show that clusterability can serve as a proxy for alignment accuracy, leading us to conclude that better structured spaces drive better semantic applications.
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Details
- Title
- Exploring Sentence Embedding Structures for Semantic Relation Extraction
- Creators
- Alexander Kalinowski - Drexel University,College of Computing & Informatics,Philadelphia,USAYuan An - Drexel UniversityIEEE
- Publication Details
- 2021 International Joint Conference on Neural Networks (IJCNN), v 2021-
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000722581707040
- Scopus ID
- 2-s2.0-85116419054
- Other Identifier
- 991019167694204721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Engineering, Electrical & Electronic