SceneGraphParser

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SceneGraphParser is a Python toolkit for parsing natural language captions into symbolic scene graphs. It offers an easy-to-use interface and a configurable design for generating structured meaning representations.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is SceneGraphParser?

SceneGraphParser (sng_parser) is a Python toolkit designed to convert natural language sentences into symbolic scene graphs. Inspired by the Stanford Scene Graph Parser, this purely Python-based tool provides an intuitive user interface and a flexible, configurable design. It parses sentences to create graphs where nodes represent nouns (including modifiers like determiners or adjectives) and edges define relations between these nouns. The project is actively developed, with APIs subject to change, and encourages community contributions for identifying failure or corner cases in its rule-based parsing approach. It was developed for the research paper "Unified Visual-Semantic Embeddings: Bridging Vision and Language With Structured Meaning Representations."

Best used for

Ideal for developers and data scientists who need to convert natural language captions into structured scene graphs, extract relationships between entities, and analyze textual data symbolically. Especially valuable for research in visual-semantic embeddings and applications requiring structured meaning representations from text.

Common actions

parse natural language

generate symbolic representations

extract scene graphs

process text data

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Capabilities

Key features

Parse natural language
Generate scene graphs
Python-based toolkit
Configurable design
Dependency parsing

Target Audience

developerdata scientiststudentprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary difference between SceneGraphParser and the Stanford Scene Graph Parser?

SceneGraphParser is written purely in Python, offering an easy-to-use interface and configurable design, unlike the original Stanford version. It aims to provide a more accessible and flexible toolkit for developers working with scene graph generation from natural language.

How does SceneGraphParser represent the generated scene graphs?

The tool represents scene graphs using standard Python dictionaries and lists. This approach prioritizes ease of integration into existing Python projects and simplifies handling the results without needing special serialization or deserialization processes.

What kind of input does SceneGraphParser expect?

SceneGraphParser takes natural language sentences or captions as input. It then processes these sentences using dependency parsing to identify nouns, their modifiers, and the relations between them, ultimately forming a structured scene graph.

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