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SLING - A natural language frame semantics parser
https://github.com/google/sling

frame-semantic-parsing jit-compiler machine-learning natural-language-processing natural-language-understanding neural-network nlp

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SLING - A natural language frame semantics parser

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# SLING - A natural language frame semantics parser



## The SLING Project has moved



The SLING project has moved to https://github.com/ringgaard/sling. 



Please refer to the above repo for the latest developments on SLING.



## Background



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](https://www.wikidata.org) knowledge base.

We use [frame semantics](doc/guide/frames.md) 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](doc/guide/wikiflow.md) can take a raw dump of Wikidata

and [convert](doc/guide/wikiflow.md#wikidata-import) this into one big frame

graph.

This can be loaded into memory so we can do fast graph traversal for inference

and reasoning over the knowledge base.

The Wiki flow pipeline can also take raw Wikipedia dumps and

[convert](doc/guide/wikiflow.md#wikipedia-import-and-parsing) these into a set

of documents with structured annotations extracted from the Wiki markup. This

also produces [phrase tables](doc/guide/wikiflow.md#name-and-phrase-tables) that

are used for mapping names to entities.

There is a [SLING Python API](doc/guide/pyapi.md) for accessing all this

information and we also have a [bot](python/wikibot) for uploading extracted

facts to Wikidata.



## The SLING Parser



The SLING parser is used 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.



![SLING neural network architecture.](./doc/report/network.svg)



The SLING framework includes an efficient and scalable

[frame store](doc/guide/frames.md) implementation as well as a

[neural network JIT compiler](doc/guide/myelin.md) for fast training and 

parsing.



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](doc/guide/install.md)

  * [CASPAR frame semantics parser](doc/guide/caspar.md)

  * [Semantic frames](doc/guide/frames.md)

  * [SLING Python API](doc/guide/pyapi.md)

  * [Myelin neural network JIT compiler](doc/guide/myelin.md)

  * [Wikipedia and Wikidata processing](doc/guide/wikiflow.md)



## Credits



Original authors of the code in this package include:



*   Michael Ringgaard

*   Rahul Gupta

*   Anders Sandholm