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CoderTraining Schmid et al



Measuring Implicit Causality: The Linguistic Category Model 
Jeannette Schmid, Klaus Fiedler, Gün Semin, & Birte Englich 
Table of Contents 
LCM – A Short Description 

The Units of Categorization 

Segmentation 

Person-Reference 

Valence 

The Coding Categories 

Descriptive Action Verbs (DAV) 

Interpretive Action Verbs (IAV) 

State Verbs (SV) 

Adjectives (ADJ) 

Special Syntactical Forms and Constructions 
10 
Nouns derived from Verbs 
10 
Nouns as Adjectivals 
10 
Participles 
10 
Additional Categorization 
11 
Passive/Inactive Voice 
11 
Moods 
11 
Idioms 
11 
Discretionary Rules for Special Text-Types 
12 
Descriptions of Fights in Professional Wrestling 
12 
Critical Evaluation of Theories in a Scientific Journal 
12 
Partner Descriptions in a Clinical Interview 
12 
Objectivity 
12 
References: 
12 
Coded Text Examples 
13 
Dr. Van Helsing’s Memorandum (from Bram Stoker’s Dracula) 
13 
Riddles in the Dark (from J. R. R. Tolkien’s The Hobbit): 
15 
Building a Home on the Island (from D. Defoe’s Robinson Crusoe): 
16 


Measuring Implicit Causality: The Linguistic Category Model 
Jeannette Schmid, Klaus Fiedler, Gün Semin & Birte Englich 
Several studies have so far focussed on the impact of linguistic categories on attribution of 
causality. The following guidelines are meant to facilitate the application of the Linguistic 
Category Model to ”natural language”, that is to language samples drawn from real media 
reports, interviews and the like. Whereas interpersonal language was the model’s original 
domain, recent work has successfully broadened the scope by applying the model to non-personal 
language. 

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