Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text
| Autor: | Mihai Surdeanu, Marco Antonio Valenzuela-Escárcega, George Caique Gouveia Barbosa, Zechy Wong, Rebecca Sharp, Gus Hahn-Powell, Dane Bell |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information retrieval
Computer science Process (engineering) 02 engineering and technology 03 medical and health sciences Multi domain 0302 clinical medicine Knowledge graph Order (exchange) 0202 electrical engineering electronic engineering information engineering Text messaging 020201 artificial intelligence & image processing 030212 general & internal medicine Causal model |
| Zdroj: | NAACL-HLT (Demonstrations) |
| Popis: | Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62%, which demonstrates the value of mining multilingual scientific information. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|