This code accompanies my Master's thesis 'The influence of the simplicity/informativeness trade-off on the semantic typology of quantifiers' for the MSc Logic at the University of Amsterdam.
The code consists of the following parts:
Generation:
- Expression generation from a grammar
- Generating quantifiers with presupposition by combining expressions
- Sampling expressions into languages
- Generating optimal languages using an evolutionary algorithm
Measuring of expressions:
- Simplicity
- Informativeness
- Monotonicity
- Conservativity
Measuring of quantifiers with presupposition:
- Simplicity
- Informativeness
Measuring of languages:
- Simplicity
- Informativeness
- Monotonicity
- Conservativity
- Naturalness
- Optimality wrt a Pareto front
Python 3.5. Get the required packages by running pip install.
Almost all code requires three main parameters:
- A path to the Experiment Setup (see the folder ExperimentSetups). This specifies the grammar, using operators from
Operator.py - The model size
- The maximum quantifier length
This will put the results of said code in results/[ExperimentSetupName]_length=[length]_size=[size]
generate.py is used to generate expressions.
merge_presuppositions.py is used to combine these into presuppositions. This requires expressions.
languages.py is used to sample languages. This requires expressions.
generate_evolutionary.py is used to approximate the Pareto Front using an evolutionary algorithm. This requires expressions and their complexities.
Expressions are measured using the Code/IndividualQuantifiers/measure_expression_*.py files. This saves the results in a separate file.
The simplicity and informativeness of quantifiers with presuppositions are measured using measure.py.
Languages are measured using the Code/IndividualQuantifiers/measure_*.py files. Most require the expressions to be measured first (e.g. to measure the monotonicity of languages one first needs to measure the monotonicity of expressions).
Code/IndividualQuantifiers/generate.py ExperimentSetups/Logical.json 12 10
Code/IndividualQuantfiers/measure_expression_complexity.py ExperimentSetups/Logical.json 12 10
Code/IndividualQuantifiers/measure_expression_informativeness.py ExperimentSetups/Logical.json 12 10
Code/IndividualQuantifiers/Analysis/plot_expressions.py ExperimentSetups/Logical.json 12 10
Code/IndividualQuantifiers/generate.py ExperimentSetups/Logical.json 7 10
Code/IndividualQuantifiers/merge_presuppositions.py ExperimentSetups/Logical.json 7 10
Code/IndividualQuantifiers/measure.py ExperimentSetups/Logical.json 7 10
# Generate the expressions and measure them
Code/IndividualQuantifiers/generate.py ExperimentSetups/Final.json 12 10
Code/IndividualQuantfiers/measure_expression_complexity.py ExperimentSetups/Final.json 12 10
Code/IndividualQuantifiers/measure_expression_monotonicity.py ExperimentSetups/Final.json 12 10
Code/IndividualQuantifiers/measure_expression_conservativity.py ExperimentSetups/Final.json 12 10
Code/IndividualQuantifiers/generate_natural_expressions.py ExperimentSetups/Final.json 12 10
# Generate using evolutionary algorithm: 2000 samples, 100 generations
Code/Languages/generate_evolutionary ExperimentSetups/Final.json 12 10 10 2000 100 -m 3 --name=evolutionary
# Sample the quasi-natural languages
Code/Languages/Languages.py --indices=natural ExperimentSetups/Final.json 12 10 10 --sample=2000 --name=natural_2000
Code/Languages/measure.py ExperimentSetups/Final.json 12 10 10 --name=natural_2000 wordcomplexity simmax
# Sample random languages
Code/Languages/Languages.py ExperimentSetups/Final.json 12 10 10 --sample=2000 --name=regular_2000
Code/Languages/measure.py ExperimentSetups/Final.json 12 10 10 --name=regular_2000 wordcomplexity simmax
# Measure optimality and compose into pandas table
Code/Languages/Analysis/measure_moo_performance.py ExperimentSetups/Final.json 12 10 exp1 evolutionary_2000 natural_2000 regular_2000
This yields the pandas table exp1.csv
# Sample languages with a uniform distribution over naturalness
Code/Languages/sample_indexset_degrees.py ExperimentSetups/Final.json 12 10 natural 10 8000 --name=natural_gradual_2000
# Measure the various properties
Code/Languages/measure.py ExperimentSetups/Final.json 12 10 10 --name=natural_gradual_2000 wordcomplexity simmax
Code/Languages/measure_monotonicity.py ExperimentSetups/Final.json 12 10 10 --name=natural_gradual_2000
Code/Languages/measure_conservativity.py ExperimentSetups/Final.json 12 10 10 --name=natural_gradual_2000
# Measure optimality and compose into pandas table
Code/Languages/Analysis/measure_moo_performance.py ExperimentSetups/Final.json 12 10 exp2 evolutionary_2000 natural_gradual_2000
This yields the pandas table exp2.csv