ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages
| Autor: | Colin Lockard, Xin Luna Dong, Hannaneh Hajishirzi, Prashant Shiralkar |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Information retrieval Generalization Computer science Subject (documents) 02 engineering and technology computer.software_genre Semantics Relationship extraction Computer Science - Information Retrieval Information extraction 020204 information systems Web page 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Representation (mathematics) computer Computation and Language (cs.CL) Information Retrieval (cs.IR) |
| Zdroj: | ACL |
| DOI: | 10.48550/arxiv.2005.07105 |
| Popis: | In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for "zero-shot" open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical. Comment: Accepted to ACL 2020 |
| Databáze: | OpenAIRE |
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