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The aim of the SLING project is to learn to read and understand Wikipedia articles in many languages for the purpose of knowledge base completion, e.g. adding facts mentioned in Wikipedia (and other sources) to the Wikidata knowledge base. We use frame semantics as a common representation for both knowledge representation and document annotation. The SLING parser can be trained to produce frame semantic representations of text directly without any explicit intervening linguistic representation. The SLING project is still work in progress. We do not yet have a full system that can extract facts from arbitrary text, but we have built a number of the subsystems needed for such a system. The SLING frame store is our basic framework for building and manipulating frame semantic graph structures. The Wiki flow pipeline can take a raw dump of Wikidata and convert this into one big frame graph.
Features
- The SLING parser is used for annotating text with frame semantic annotations
- Documentation available
- Examples available
- The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast training and parsing
- The SLING parser can be trained to produce frame semantic representations of text directly without any explicit intervening linguistic representation
Project Samples
