Using Letter Positional Probabilities to Assess French Word Complexity

Michael Dalvean

cs.CLOct 20, 2026

Using Letter Positional Probabilities to Assess French Word Complexity

Authors: Michael Dalvean

Abstract

We use Letter Positional Probabilities (LPPs) to examine the orthographic differences between simple and complex French words. We draw from a sample of 38,000 French words scored by reaction time on a lexical decision task. We take the 5000 highest reaction time words and the 5000 lowest as a proxy for high and low word complexity groups respectively. We create 18 variables based on the structure of the words, such as length and number of syllables. We also reduce each word to a string of binary values by one-hot encoding each letter according to the 42 characters of the standard French alphabet up to a length of 25 letters. Thus, each word is represented by 42 x 25 binary characters. We then create LPPs by looking at the average binary scores for simple as opposed to complex words for all letter/positions. We find that there is a high number of LPPs that are associated with word complexity. For example, we find that most accented letters are associated with simpler words. We examine the effects of structural and LPP variables in classifiers and find that word length alone enables a classification accuracy of 77.27%, while adding the binary variables from the first 6 letter positions provided an accuracy of 83.95%. We discuss possible explanations as to how complexity could be encoded in orthography.

Figures & tables

	Gzip	Compress	DLETDC	ETDC	SCDC	CAFTS
Dutch	2.86	3.04	2.91	2.84	2.80	3.23
English	3.06	3.22	2.82	2.78	2.70	3.23
Finnish	3.03	3.25	4.12	4.02	3.99	3.36
French	2.87	3.11	3.15	3.09	3.04	3.18
German	2.98	3.24	3.40	3.32	3.28	3.45
Italian	3.10	3.24	3.29	3.21	3.17	3.29

Table 1: Compression ratios (in bits/character), compression and decompression times (in seconds).

Variable	Mean – Low Complexity ( $n=5{,}000$ )	Mean – High Complexity ( $n=5{,}000$ )	$p$ value (Bonf. $\times 459$ )
o6	0.011	0.051	$<.0001$
e5	0.168	0.092	$<.0001$
n2	0.035	0.086	$<.0001$
a6	0.042	0.094	$<.0001$
é6	0.056	0.017	$<.0001$
i1	0.020	0.060	$<.0001$

Table 2: Low- and high-complexity French words: mean values for positional letter features at positions <7.

Feature	Low Mean	High Mean	Direction
é6	0.056	0.017	Low $\uparrow$
ê2	0.007	0.000	Low $\uparrow$
â2	0.008	0.001	Low $\uparrow$
é5	0.040	0.023	Low $\uparrow$
û3	0.005	0.000	Low $\uparrow$
â6	0.000	0.004	High $\uparrow$

Table 3: Low- and high-complexity French words: Significant accented-character features at positions < 7

Feature	Low Mean	High Mean	Direction
a4	0.069	0.102	High $\uparrow$
a6	0.042	0.094	High $\uparrow$
i5	0.089	0.127	High $\uparrow$
i6	0.053	0.105	High $\uparrow$
o5	0.040	0.078	High $\uparrow$
o6	0.011	0.051	High $\uparrow$

Table 4: Low- and high-complexity French words: Significant unaccented-character features at positions < 7

Vowels
Feature	Low Mean	High Mean	Direction
o6	0.011	0.051	High $\uparrow$
a6	0.042	0.094	High $\uparrow$
i6	0.053	0.105	High $\uparrow$
â6	0.000	0.004	High $\uparrow$
e6	0.161	0.097	Low $\uparrow$

Table 5: Low- and high-complexity French words: Significant vowel and consonant features at position 6.

Feature	Low Mean	High Mean	Direction
i1	0.020	0.060	High $\uparrow$
j1	0.019	0.005	Low $\uparrow$
v1	0.044	0.021	Low $\uparrow$
l1	0.032	0.017	Low $\uparrow$
a1	0.063	0.088	High $\uparrow$
f1	0.053	0.034	Low $\uparrow$

Table 6: Low- and high-complexity French words: Significant character features at position 1.

Feature	Low Mean	High Mean	Direction
n2	0.035	0.086	High $\uparrow$
o2	0.195	0.129	Low $\uparrow$
l2	0.043	0.022	Low $\uparrow$
ê2	0.007	0.000	Low $\uparrow$
m2	0.015	0.032	High $\uparrow$
â2	0.008	0.001	Low $\uparrow$

Table 7: Low- and high-complexity French words: Significant character features at position 2.

Feature	Low Mean	High Mean	Direction
y3	0.014	0.004	Low $\uparrow$
u3	0.087	0.060	Low $\uparrow$
h3	0.005	0.014	High $\uparrow$
t3	0.061	0.085	High $\uparrow$
û3	0.005	0.000	Low $\uparrow$
r3	0.082	0.108	High $\uparrow$

Table 8: Low- and high-complexity French words: Significant character features at position 3

Feature	Low Mean	High Mean	Direction
a4	0.069	0.102	High $\uparrow$
r4	0.061	0.088	High $\uparrow$
o4	0.060	0.084	High $\uparrow$

Table 9: Low- and high-complexity French words: Significant character features at position 3

Feature	Low Mean	High Mean	Direction
e5	0.168	0.092	Low $\uparrow$
o5	0.040	0.078	High $\uparrow$
i5	0.089	0.127	High $\uparrow$
m5	0.012	0.028	High $\uparrow$
r5	0.077	0.108	High $\uparrow$
z5	0.007	0.001	Low $\uparrow$

Table 10: Low- and high-complexity French words: Significant character features at position 5

Classifier	Preprocessing	Model specification
Support Vector Machine	Median imputation; standardisation	Linear SVM ( LinearSVC ); $C=1.0$ ; maximum 10,000 iterations.
Random Forest	Median imputation	500 decision trees; square-root feature sampling at each split; all available processor cores used during training.
Neural Network	Median imputation; standardisation	Feed-forward network with three hidden layers containing 128, 64 and 32 units, respectively (ReLU activation). Dropout of 0.20 after the first two hidden layers. Single sigmoid output unit; Adam optimiser; binary cross-entropy loss.

Table 11: Details of SVM, RF and NN classifiers

Feature Set	NN	SVM	RF
Length	0.7727	0.7727	0.7727
Structural	0.7785	0.7775	0.7620
Structural + All Positions	0.8111	0.8031	0.8085
First 6 Positions	0.7657	0.6932	0.8012
All Positions	0.8091	0.7966	0.8364
Structural + First 6 Positions	0.8184	0.7951	0.8031

Table 12: Classification accuracy details

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Variable	Mean – Low Complexity	Mean – High Complexity	$p$ value
	( $n=5{,}000$ )	( $n=5{,}000$ )	(Bonf. $\times 459$ )
LENGTH	6.694	9.766	$<.0001$
SYLLABLES	2.071	3.050	$<.0001$
UNIQUE_LETTERS	5.922	7.562	$<.0001$
LETTER_DIVERSITY	0.897	0.790	$<.0001$
REPEATED_LETTERS	0.773	2.204	$<.0001$

Appendix

Table 13: Mean values for low- and high-complexity French words and Bonferroni-adjusted

p

-values.

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Abstract

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