SemanticSpace
Semantic mapping site that places phrases in 2D using Jev's scores for how strongly each relates to two chosen axis concepts.
This summary is based on how curated lists describe the project; its README was not available to review.
Overview
SemanticSpace is a web page listed in Jev directories. According to the list summary, it places phrases on a two-dimensional map by asking Jev how strongly each phrase relates to two chosen axis concepts and using those two scores as the coordinates.
No page content was fetched, so the exact question form, scale and interface details are unknown. It is an example of using decision-model scores as continuous features rather than as labels.
What the decision model decides
| Type | Question | Answers |
|---|---|---|
| Score | How strongly does this phrase relate to axis concept A (and to concept B)? | — |
How it works
- Choose two axis concepts
- For each phrase, ask Jev to score its relation to each concept
- Plot phrases using the two scores as x and y
Strengths
- Turns typed judgments into an interpretable visual map
Caveats
- Grounded only in a one-line summary
- Scores are model judgments, not measured semantics
At a glance
- Type
- Article
- Runtime
- Web page
Use cases and integrations
How curated lists describe it
Semantic mapping: places phrases in 2D by asking Jev how strongly each one relates to two chosen axis concepts and using those scores as coordinates.
yibie/awesome-jev
Frequently asked questions
What does Jev decide?
How strongly each phrase relates to each of two chosen concepts.
How are the coordinates produced?
The two scores become the x and y positions.
Is the code available?
Not stated in the available information.
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