Semantic Taxonomy Induction
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 7 July 2006
- Authors
- Rion Snow, Dan Jurafsky, and Andrew Y. Ng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language Structure Modeling
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 0.1K
- Training data
- tokens
The main learning algorithm is a logistic classifier. The input is a matrix M, where the rows are pairs of words, and the columns (variables) are counts of occurrences of synthetic dependency paths between those two words. Since there are on the order of 10~100 different types of syntactic relationships, this is the number of length-1 paths, and thus the number of parameters if only length-1 paths are used. However, if the length of the paths considered is longer (say, 5), then the parameters …
[Classification task] The labeled training set is constructed by labeling the collected feature vectors as positive “known hypernym” or negative “known non-hypernym” examples using WordNet 2.0; 49,922 feature vectors were labeled as positive training examples, and 800,828 noun pairs were labeled as negative training examples. 800,828 + 49,922 = 850750
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 571
Sources
Where this record came from and when it was last checked.
- Reference
- Semantic Taxonomy Induction from Heterogenous Evidence
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Semantic Taxonomy Induction was published by Stanford University, in United States of America, in July 2006. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language Structure Modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Semantic Taxonomy Induction — common questions
What is Semantic Taxonomy Induction used for?
Semantic Taxonomy Induction works in Language, and is recorded as handling language Structure Modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Semantic Taxonomy Induction?
None. Semantic Taxonomy Induction is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Semantic Taxonomy Induction open source?
The licensing for Semantic Taxonomy Induction was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Semantic Taxonomy Induction have?
Semantic Taxonomy Induction has 0.1K parameters. The main learning algorithm is a logistic classifier. The input is a matrix M, where the rows are pairs of words, and the columns (variables) are counts of occurrences of synthetic dependency paths between those two words. Since there are on the order of 10~100 different types of syntactic relationships, this is the number of length-1 paths, and thus the number of parameters if only length-1 paths are used. However, if the length of the paths considered is longer (say, 5), then the parameters would be on the order of (10~100)^5. It's not clear to me which is the case. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Semantic Taxonomy Induction?
Semantic Taxonomy Induction was published by Stanford University, based in United States of America, categorised as academia.
When was Semantic Taxonomy Induction released?
Semantic Taxonomy Induction was published in July 2006. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.