The Tool for Automatic Measurement of Morphological Information (TAMMI)
Scott A Crossley1, Rurik Tywoniw2, Joon Suh Choi3
1Vanderbilt University, Nashville, USA. scott.crossley@vanderbilt.edu.
Behavior Research Methods
|December 29, 2023
Summary
The Tool for Automatic Measurement of Morphological Information (TAMMI) quantifies word structure. TAMMI effectively predicts reading ease and vocabulary proficiency by analyzing morpheme counts and variety.
Area of Science:
- Computational Linguistics
- Psycholinguistics
- Natural Language Processing
Background:
- Understanding word structure (morphology) is crucial for reading and language proficiency.
- Existing methods for analyzing morphological complexity can be labor-intensive.
- The MorphoLex database provides rich lexical information for morphological analysis.
Purpose of the Study:
- To document and assess the Tool for Automatic Measurement of Morphological Information (TAMMI).
- To evaluate TAMMI's ability to predict reading ease and vocabulary proficiency.
- To explore the relationship between morphological variables and language skills.
Main Methods:
- TAMMI was developed to calculate various morphological measures (e.g., morpheme counts, variety, complexity, type-token counts).
- Two studies were conducted using TAMMI, controlling for word frequency.
- Study 1: Assessed reading ease in ~5000 reading excerpts.
- Study 2: Assessed vocabulary proficiency in ~7000 essays by English-language learners (ELLs).
Main Results:
- Morphological variables, word frequency, affix frequency, and morpheme counts explained 40% of the variance in reading scores.
- Morpheme count, morpheme variety, and root count explained 21% of the variance in vocabulary proficiency assessments for ELLs.
Conclusions:
- TAMMI is a valuable tool for automatically measuring morphological information.
- Morphological variables significantly contribute to explaining reading ease and vocabulary proficiency.
- TAMMI's metrics can enhance our understanding of language processing and learning.
