BabakScrapes commited on
Commit
0ced955
·
verified ·
1 Parent(s): c3c14c2

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +19 -1
README.md CHANGED
@@ -1,4 +1,22 @@
1
  ---
2
  sdk: gradio
3
  python-version: 3.8
4
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  sdk: gradio
3
  python-version: 3.8
4
+ ---
5
+ # Coding-Free Automated Anecdotal Discourse Classification
6
+ ## Generalized versus Anecdotal Contexts
7
+ People may offer generalized or anecdotal contexts when making statements. Anecdotal context includes narratives, personal anecdotes, case histories,
8
+ personal stories, personal assertions and testimonies. An example is when someone argues for legalization of marijuana based on what happened to their
9
+ 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
10
+ entire categories of people or things. An example is saying marijuana should be legalize because it would improve the economy.
11
+ ## Generalized versus Anecdotal Statements in Language
12
+ 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)
13
+ that more or less anecdotal statements can be identified using the combination of four clause-level linguistic features:
14
+ 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).
15
+ 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).
16
+ 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).
17
+ 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.
18
+ 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.
19
+ ## AI-based Automatic Identification of Anecdotal Statements in Natural Text
20
+ This repository contains a coding-free tool for deriving labels for the above features from any text.
21
+ It uses the transformer neural networks developed in the dissertation discussed above. Note that this tool is best used for analysis.
22
+ [This other tool](https://huggingface.co/spaces/BabakScrapes/Anecedotal_Discourse_Classifier_Demo) would be more appropriate for demonstration purposes.