SLING - A natural language frame semantics parser
SLING is a parser for annotating text with frame semantic annotations. It is a general transition-based frame semantic parser using bi-directional LSTMs for input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. It is a jointly trained model using only the text tokens as input and the transition system has been designed to output frame graphs directly without any intervening symbolic representation.
The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast parsing at runtime.
A more detailed description of the SLING parser can be found in this paper:
- Michael Ringgaard, Rahul Gupta, and Fernando C. N. Pereira. 2017. SLING: A framework for frame semantic parsing. http://arxiv.org/abs/1710.07032.
More information ...
- Installation and building
- Training a parser
- Running the parser
- Semantic frames
- SLING Python API
- Myelin neural network JIT compiler
- Wikipedia and Wikidata processing
Original authors of the code in this package include:
- Michael Ringgaard
- Rahul Gupta
- Anders Sandholm