# google/sling

SLING - A natural language frame semantics parser

Repository: https://github.com/google/sling
Canonical: https://ross.abutalabs.com/products/sling
Language: C++
License: Apache-2.0
License Family: permissive
Topics: natural-language-understanding, natural-language-processing, neural-network, machine-learning, jit-compiler, frame-semantic-parsing, nlp
Archived: true
Last push: 2021-01-22T21:43:36+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3417, "days_push": 2049, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1930, forks 264 (observed 2026-08-28T04:05:56.045186+00:00)

## What it is
SLING is a natural language frame semantics parser that annotates text with frame semantic graph representations using bi-directional LSTMs and a transition-based decoder. It includes a scalable frame store for knowledge representation, a Wiki flow pipeline for processing Wikidata and Wikipedia dumps, and a neural network JIT compiler for fast training and parsing.

## Use cases
- parse text into frame semantic representations
- extract facts from Wikipedia articles for knowledge base completion
- convert Wikidata dumps into an in-memory frame graph
- map entity names and phrases to knowledge base entities
- train a custom semantic parser on annotated documents
- upload extracted facts to Wikidata via a bot

## When to choose
- you need frame semantic parsing with direct graph output from raw text
- you are doing knowledge base completion with Wikidata or Wikipedia data
- you want an efficient in-memory frame store for graph traversal and reasoning
- you need a self-contained neural parser with a JIT compiler for fast inference

## When to avoid
- you need a general-purpose production NLP pipeline with broad language support
- you want a maintained project with active development and support
- you need simple off-the-shelf NER or dependency parsing rather than frame semantics
- your stack requires a pure Python or managed-language solution

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, parser, llm-training, data-science
- domain: machine-learning
- platform: cpp, python
- tags: frame-semantics, knowledge-base-completion, wikidata, wikipedia, neural-network-parser, frame-store, jit-compiler, natural-language-processing, knowledge-graphs, linux, macos

## Member repositories
- google/sling (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:56.045186+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:08:47.276767+00:00, confidence not recorded.
  - readme: https://github.com/google/sling (fetched 2026-08-28T04:05:56.045186+00:00, sha 75d85ec81695)
- Data as of 2026-08-30T08:39:29.467469+00:00.