graykode / graykode/nlp-tutorial

Which kind of model is better for keyword-set classification?

Open
#43 0 comments 0 reactions 0 assignees View on GitHub
Dominant language
Jupyter Notebook
Stars
14.9k
Forks
3.9k
PR merge metrics
No merged PRs in 30d

Description

There exists a similar task that is named text classification.

But I want to find a kind of model that the inputs are keyword set. And the keyword set is not from a sentence.

For example:
```
input ["apple", "pear", "water melon"] --> target class "fruit"
input ["tomato", "potato"] --> target class "vegetable"
```
Another example:
```
input ["apple", "Peking", "in summer"] --> target class "Chinese fruit"
input ["tomato", "New York", "in winter"] --> target class "American vegetable"
input ["apple", "Peking", "in winter"] --> target class "Chinese fruit"
input ["tomato", "Peking", "in winter"] --> target class "Chinese vegetable"
```

Thank you.

Contributor guide

Open the contributing guide

Research direction

The issue names no file, test, or entry point. Start by reviewing the referenced text-classification material and clarify how keyword sets should be represented and evaluated; the work is complete when a specific model direction and implementation scope are agreed.

Written by the indexing model from the issue text.

Assessment

Domain
machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.