|
|
--- |
|
|
sdk: gradio |
|
|
python-version: 3.8 |
|
|
--- |
|
|
# Coding-Free Automated Anecdotal Discourse Classification |
|
|
## Generalized versus Anecdotal Contexts |
|
|
People may offer generalized or anecdotal contexts when making statements. Anecdotal context includes narratives, personal anecdotes, case histories, |
|
|
personal stories, personal assertions and testimonies. An example is when someone argues for legalization of marijuana based on what happened to their |
|
|
cousin who was jailed for possession. Generalized context, on the other hand, involves broad assertions about the world, including logical conclusions, reliable causal links and claims about |
|
|
entire categories of people or things. An example is saying marijuana should be legalize because it would improve the economy. |
|
|
## Generalized versus Anecdotal Statements in Language |
|
|
You can think of anecdotal contexts as statements focused on more limited physical and temporal spaces. I argue in [this doctoral dissertation](https://www.researchgate.net/publication/356109604_Taking_the_High_Road_A_Big_Data_Investigation_of_Natural_Discourse_in_the_Emerging_US_Consensus_about_Marijuana_Legalization) |
|
|
that more or less anecdotal statements can be identified using the combination of four clause-level linguistic features: |
|
|
1. Genericity: Whether the entity the clause is about is a generic category (e.g., humanity; generic) or specific instances of one (e.g., one's friends; specific). |
|
|
2. Eventivity: (a.k.a. fundamental semantic aspect): Whether the verb complex describes a state (e.g., God is benevolent; stative) or an event (e.g., I went to Nebraska; dynamic). |
|
|
3. Boundedness: Whether any events presented have a temporal boundary (e.g., I ate this morning; episodic) or do not (e.g., God loves us; static). |
|
|
4. Habituality: Events can also be repetitive (e.g., I went to school there for years; habitual) or not (I stopped by; episodic). I have combined this feature with boundedness given their close relation. |
|
|
The most anecdotal content is theorized to involve specific entities with bounded, non-habitual events. But different mixtures of the dimensions can provide gradations of anecdotal focus. |
|
|
## AI-based Automatic Identification of Anecdotal Statements in Natural Text |
|
|
This repository contains a coding-free tool for deriving labels for the above features from any text. |
|
|
It uses the transformer neural networks developed in the dissertation discussed above. Note that this tool is best used for analysis. |
|
|
[This other tool](https://huggingface.co/spaces/BabakScrapes/Anecedotal_Discourse_Classifier_Demo) would be more appropriate for demonstration purposes. Links and scripts for the corpus it was trained on can be found in [this repository](https://github.com/sfeucht/annotation_evaluation). |
|
|
|
