Feature reference

Every feature the library can produce, grouped as it is grouped in the code. This page is generated from the registry, so it cannot fall out of step with what describe_feature() returns.

Reading the columns

Column	Meaning
Key	The dict key analyze() returns
Description	One-sentence definition
Formula	The computation, in words
Requires	Minimum data needed; below it the feature returns nan. A "word" is the library's default word (Concepts)
Source	Short citation. — means it is not a named measure from the literature

Whether a feature's number has been checked against the number its source published is a separate question — see the verification report.

Groups

Group	Keys	What it covers
lexical	38	Lexical richness & frequency
frequency_structure	13	Frequency structure (h-point family, Popescu & Altmann)
sentence	7	Sentence statistics
paragraph	3	Paragraph structure
pos	12	Part-of-speech ratios
syntactic	7	Discourse & syntax
syntactic_dep	16	Dependency tree (distance, depth, sentence-final POS)
morphological	19	Morphological style (spaCy)
morphological_zeyrek	23	Morphological style (Zeyrek, TR only)
phonetic	14	Phonetic patterns
readability	11	Readability scores
punctuation	20	Punctuation & digits
chars	dynamic	Character frequency vector [dynamic: one key per letter — TR 29, EN 26]
custom_ngrams	dynamic	User-defined n-gram counts [dynamic: ngram_{...}_count]

lexical — Lexical richness & frequency

38 keys.

Key	Description	Formula	Requires	Source
lemma_count	number of distinct lemmas	V over lemmas	at least 1 word	—
word_count	number of words	words	non-empty text (0 when it has no words)	de Vel (2000) Table 2, attribute 1 "Total number of words"; Zheng et al. (2006) Table 3, p.385, no. 54 "Total number of words (M)"
type_count	number of distinct words (V)	V	non-empty text (0 when it has no words)	Tweedie & Baayen (1998) p.325, the vocabulary size V(N); Zheng et al. (2006) Table 3, p.385, no. 60 "Total different words/M", here not divided by M
hapax_count	number of words occurring once (V1)	V1	non-empty text (0 when it has no words)	Tweedie & Baayen (1998) p.325, V(1, N) "the number of hapax legomena"; Zheng et al. (2006) Table 3, p.385, no. 61 "Hapax legomena" ("Frequency of once-occurring words")
dislegomena_count	number of words occurring exactly twice (V2)	V2	non-empty text (0 when it has no words)	Tweedie & Baayen (1998) p.329, V(2, N) "dis legomena"; Zheng et al. (2006) Table 3, p.385, no. 62 "Hapax dislegomena" ("Frequency of twice-occurring words")
word_len_mean	mean word length in characters	sum(len(w)) / N	at least 1 word	Mendenhall (1887) p.237 "mean word-length"; computed as letters per word, p.241
ttr	type-token ratio; falls as the text grows	V / N	at least 1 word	Malvern et al. (2004); QUITA §6.1.1
mattr	moving-average TTR	mean TTR of every sliding window of mattr_window words	at least 100 words (2 x mattr_window)	Covington & McFall (2010); default window 50 — C&M recommend a window of 500; 50 is used here so that texts of 100+ words can be measured (mattr needs 2 × window)
herdan_c	Herdan's C (LogTTR)	log(V) / log(N)	at least 2 words	Herdan (1960/1964), as cited in Tweedie & Baayen (1998) p.327, eq. (5)
sichel_s	share of types occurring exactly twice	V2 / V	at least 1 word	Sichel (1975); formula from Malvern et al. (2004) eq. 3.10
zipf_exponent	Zipf slope	abs(slope) of least-squares fit log f(r) ~ log r	at least 10 distinct words	Piantadosi (2014) eq. (2); the same source criticises estimating r and f(r) from the same corpus
zipf_r2	fit quality of the Zipf line	R² of that fit	at least 10 distinct words, not all equally frequent	Piantadosi (2014) eq. (2); the same source criticises estimating r and f(r) from the same corpus
zipf_mandelbrot_q	Zipf-Mandelbrot shift	q minimising the residual of log f ~ log(r + q), grid 0–10 step 0.1	at least 10 distinct words	Piantadosi (2014) eq. (2); the same source criticises estimating r and f(r) from the same corpus
zipf_mandelbrot_s	Zipf-Mandelbrot slope	abs(slope) at that q	at least 10 distinct words	Piantadosi (2014) eq. (2); the same source criticises estimating r and f(r) from the same corpus
mtld	measure of textual lexical diversity	mean words per factor (TTR drops to mtld_threshold), forward and backward averaged	at least 100 words (mtld_min_tokens) with some repetition	McCarthy (2005) is the dissertation that introduced the measure — its abstract (p.vii) reads "we introduce and test a new measure of lexical diversity: the measure of textual, lexical diversity (MTLD)"; the body of the dissertation could not be obtained, so no page is given. The procedure implemented follows McCarthy & Jarvis (2010) pp.383–385
dugast_u	Dugast's Uber index	(ln N)^2 / (ln N - ln V)	at least 2 words, at least one repeated	Dugast (1978), as cited in Malvern et al. (2004) eq. 2.7
guiraud_r	Guiraud's root TTR	V / sqrt(N)	at least 1 word	Guiraud (1954) p.53, alternative form (all word types), as cited in Daller (2010); his actual law is V/√(2N), content words only
cttr	Carroll's corrected TTR	V / sqrt(2N)	at least 1 word	Carroll (1964), as cited in Torruella & Capsada (2013) p.448
summer_s	Summer's S, log-log type-token ratio	ln(ln V) / ln(ln N)	at least 3 words and 2 distinct words	Somers (1966), as cited in Torruella & Capsada (2013) p.448, where it is named "Summer"; the source gives no logarithm base, the natural logarithm is this library's choice
maas_a2	Maas' a²; higher = more repetitive	(ln N - ln V) / (ln N)^2	at least 2 words	Maas (1972), as cited in Tweedie & Baayen (1998) p.327, eq. (7); natural logarithm, which reproduces the values in Torruella & Capsada (2013) Table 1; all logarithms in this library are natural
herdan_vm	Herdan's Vm; higher = more repetitive	sqrt(sum(f^2) / N^2 - 1 / V)	at least 1 word	Herdan (1955), as cited in Tweedie & Baayen (1998) p.330, eq. (18)
honore_r	Honoré's R; higher = more words used once, richer vocabulary	100 * ln N / (1 - V1 / V)	at least 1 word occurring more than once	Honoré (1979), as cited in Tweedie & Baayen (1998) p.329, eq. (11)
heaps_beta	vocabulary growth rate	least-squares slope of log V ~ log N over prefixes every heaps_step words, not clipped	at least 300 words (heaps_min_tokens)	Heaps (1978), as cited in Manning et al. (2008) §5.1.1
entropy	Shannon entropy of word frequencies	-sum(p * ln p)	at least 1 word	Shannon (1948), as cited in QUITA §6.1.12
yule_k	Yule's K; higher = more repetitive	10000 * (sum(f^2) - N) / N^2	at least 1 word	Yule (1944) p.53, eq. (3.22)
simpson_d	chance that two words drawn without replacement are the same type	sum(f(f-1)) / (N(N-1))	at least 2 words	Simpson (1949), as cited in Bestgen (2023)
brunet_w	Brunet's W	N^(V^-a), a = brunet_w_a	at least 1 word	Brunet (1978), as cited in Tweedie & Baayen (1998) p.328, eq. (10)
hapax_ratio	share of types occurring once	V1 / V	at least 1 word	de Vel (2000) Table 2, attribute 14 "Ratio of words used once to total number of vocabulary words"
hapax_token_ratio	share of tokens that occur once	V1 / N	at least 1 word	QUITA §6.1.6
vocd_d	voc-D	D fitted to mean TTR of random samples of vocd_sample_min–vocd_sample_max words, vocd_num_runs runs averaged	at least 50 words (vocd_min_tokens, vocd_sample_max)	Malvern et al. (2004) pp.56–57; procedure from McCarthy & Jarvis (2010) p.383
hdd	HD-D	expected TTR of a hdd_sample_size-word sample (hypergeometric)	at least 42 words (hdd_sample_size)	McCarthy & Jarvis (2007), as cited in McCarthy & Jarvis (2010) p.383
msttr	mean segmental TTR	mean TTR of full msttr_segment_size-word segments	at least 100 words (msttr_segment_size)	Johnson (1944), as cited in Malvern et al. (2004) p.25 and McCarthy & Jarvis (2010) p.385
noun_variation	noun variation NV	distinct noun lemmas / lexical words	at least 1 lexical word (NOUN, PROPN, VERB, ADJ, ADV)	Lu (2012) Table 2
verb_variation	verb variation VV1	distinct verb lemmas / verbs	at least 1 verb	Lu (2012) Table 2
adj_variation	adjective variation AdjV	distinct adjective lemmas / lexical words	at least 1 lexical word	Lu (2012) Table 2
adv_variation	adverb variation AdvV	distinct adverb lemmas / lexical words	at least 1 lexical word	Lu (2012) Table 2
wordfreq_mean	how common the lexical words are in general language	mean wordfreq Zipf score of lexical-word lemmas (unlisted = 0)	at least 1 lexical word; wordfreq installed	van Heuven et al. (2014) (Zipf scale)
wordfreq_rare_ratio	share of rare lexical words	lexical-word lemmas with Zipf score <= 3 / lexical words	at least 1 lexical word; wordfreq installed	van Heuven et al. (2014) Table 1 (Zipf ≤ 3 = low frequency)

frequency_structure — Frequency structure (h-point family, Popescu & Altmann)

13 keys.

Key	Description	Formula	Requires	Source
h_point	rank where frequency equals rank	r with f(r) = r, interpolated otherwise	at least 1 word	QUITA §6.1.2; Popescu & Altmann (2006)
vocab_richness_r1	share of the text below the h-point	1 - (F(h) - h^2 / (2N))	at least 1 word	Popescu et al. (2009) eq. 3.8
vocab_richness_r4	inverted Gini	1 - G	at least 1 word	Popescu et al. (2009) eq. 3.24
repeat_rate	chance that two words drawn with replacement are the same type	sum((f/N)^2)	at least 1 word	QUITA §6.1.4
rr_mcintosh	McIntosh relative repeat rate	(1 - sqrt(RR)) / (1 - 1/sqrt(V))	at least 2 distinct words	QUITA §6.1.5
gini_coef	inequality of word use	(V + 1 - 2 * sum(r * f(r)) / N) / V, rank 1 = most frequent	at least 1 word	QUITA §6.1.8
curve_length	arc length of the rank-frequency curve	sum(sqrt((f(r) - f(r+1))^2 + 1))	at least 1 word	QUITA §6.1.10
curve_length_r	share of the curve length below the h-point	1 - L(h) / L	at least 2 distinct words	QUITA §6.1.11
lambda_pa	length-normalised curve length	L * ln(N) / N	at least 1 word	QUITA §6.1.7; Popescu, Čech & Altmann (2011)
adjusted_modulus	distance from the h-point to the curve ends	sqrt((f1/h)^2 + (V/h)^2) / ln(N)	at least 2 words	QUITA §6.1.13
writers_view_alpha	angle at the h-point, in radians	arccos of the angle between (1, f1) and (V, 1) seen from (h, h)	at least 1 word; undefined when the h-point meets a curve end	Popescu, Mačutek & Altmann (2009) eq. 4.5
thematic_concentration	weight of content words above the h-point	sum(2(h - r') f(r')) / (h(h-1) f1), content words with r' < h	at least one repeated word	QUITA §6.2.5
secondary_thematic_concentration	weight of content words up to rank 2h	sum((2h - r') f(r')) / (h(2h-1) f1), r' <= 2h	at least 1 word	QUITA §6.2.6

sentence — Sentence statistics

7 keys.

Key	Description	Formula	Requires	Source
sent_len_mean	mean sentence length in words	mean words per sentence	at least 1 sentence with a letter	Flesch (1948) p.223, element (1) "Average Sentence Length in Words"
sent_count	number of sentences	sentences	at least 1 sentence with a letter	de Vel (2000) Table 2, attribute 3 "Number of sentences"; Zheng et al. (2006) Table 3, p.385, no. 247 "Total number of sentences"
sent_len_char_mean	mean sentence length in characters	mean len(tokens joined by single spaces)	at least 1 sentence	Zheng et al. (2006) Table 3, p.385, no. 58 "Average sentence length in terms of character"
short_sent_ratio	share of short sentences	sentences with fewer than short_sent_threshold words / sentences	at least 1 sentence with a letter	This library's threshold calibration (docs/threshold-calibration.md); TR 4, EN 8 — 15th percentile of newspaper columns under the default sentence and word rules. Note: the TR value coincides with Ateşman (1997) p.74, where the easiest text has a mean sentence length of 4 words; that is a text mean, not a threshold, so it is not the source. Calibrated on newspaper columns only
long_sent_ratio	share of long sentences	sentences with more than long_sent_threshold words / sentences	at least 1 sentence with a letter	This library's threshold calibration (docs/threshold-calibration.md); TR 17, EN 32 — 85th percentile of newspaper columns under the default sentence and word rules. Ateşman's 30 was not used: that is the mean of the hardest text, not a single-sentence threshold (in Turkish newspaper columns 30 words is above the 95th percentile, so as a threshold it would almost never fire). Calibrated on newspaper columns only
sent_len_median	median sentence length	median words per sentence	at least 1 sentence with a letter	Yule (1939) p.369, median sentence length alongside the mean
sent_len_entropy	variety of sentence lengths	Shannon entropy (nats) of the distribution of words per sentence	at least 2 sentences with a letter	Shannon (1948) — the entropy formula; applying it to the distribution of sentence lengths is this library's own decision

paragraph — Paragraph structure

3 keys.

Key	Description	Formula	Requires	Source
para_count	number of paragraphs	paragraphs (blank line = boundary)	at least 1 paragraph	Zheng et al. (2006) Table 3, p.385, no. 248 "Total number of paragraphs"
para_len_mean	mean paragraph length	mean words per paragraph (blank line = boundary)	at least 1 paragraph	Zheng et al. (2006) Table 3, p.385, no. 251 "Number of words per paragraph"
sents_per_para_mean	mean sentences per paragraph	sentences / paragraphs (sent_count / para_count)	at least 1 sentence with a letter	Zheng et al. (2006) Table 3, p.385, no. 249 "Number of sentences per paragraph"

pos — Part-of-speech ratios

12 keys.

Key	Description	Formula	Requires	Source
pos_noun_ratio	share of NOUN words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_propn_ratio	share of PROPN words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_verb_ratio	share of VERB words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_adj_ratio	share of ADJ words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_adv_ratio	share of ADV words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_det_ratio	share of DET words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_adp_ratio	share of ADP words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_aux_ratio	share of AUX words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_cconj_ratio	share of CCONJ words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_sconj_ratio	share of SCONJ words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_num_ratio	share of NUM words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
pos_intj_ratio	share of INTJ words	tag count / words	at least 1 word	de Marneffe et al. (2021) Table 1 (UPOS tag set)

syntactic — Discourse & syntax

7 keys.

Key	Description	Formula	Requires	Source
question_sent_ratio	share of sentences ending in "?"	sentences whose final mark contains "?" / sentences	at least 1 sentence	—
pronoun_ratio	share of pronoun words	PRON / words	at least 1 word	Deutsch, Jasbi & Shieber (2020) Table 6 "pronouns per word", listed among existing features; original source not traced
verb_dist_mean	mean word gap between consecutive verbs	mean difference of VERB positions	at least 2 verbs	QUITA §6.2.1
activity_ratio	activity Q	VERB / (VERB + ADJ)	at least 1 verb or adjective	QUITA §6.2.2
lexical_density	share of lexical words	(NOUN + PROPN + VERB + ADJ + ADV) / all words, PUNCT and SYM excluded	at least 1 word	Lu (2012); definition in the broad Hallidayan sense — all open-class words
posddev	how uneven the POS distribution is	population std of the 12 pos_*_ratio shares	at least 1 word	Deutsch, Jasbi & Shieber (2020) Definition 3.3 (POSDdev); computed over ratios, 12 UD tags
posdiv	how much sentences differ from the document in POS make-up	mean over sentences of KL(sentence POS ‖ document POS), nats	at least 1 word	Deutsch, Jasbi & Shieber (2020) Definition 3.4 (POSdiv); natural logarithm (nats), the source uses bits

syntactic_dep — Dependency tree (distance, depth, sentence-final POS)

16 keys.

Key	Description	Formula	Requires	Source
arc_len_mean	mean dependency distance (MDD2)	mean over sentences of mean abs(word position - head position), punctuation removed, root excluded	at least 1 sentence with 2 words	Liu (2008) eq. (1); text level from Jing & Liu (2015) p.164, eq. (3) (MDD2)
parse_depth_mean	mean hierarchical distance (MHD2)	mean over sentences of mean steps to the root, capped at max_parse_depth	at least 1 sentence with 2 words	Jing & Liu (2015) p.164, eq. (2) and (4) (MHD2)
sentfinal_noun_ratio	share of sentences ending in a NOUN	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_propn_ratio	share of sentences ending in a PROPN	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_verb_ratio	share of sentences ending in a VERB	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_adj_ratio	share of sentences ending in a ADJ	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_adv_ratio	share of sentences ending in a ADV	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_det_ratio	share of sentences ending in a DET	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_adp_ratio	share of sentences ending in a ADP	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_intj_ratio	share of sentences ending in a INTJ	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_cconj_ratio	share of sentences ending in a CCONJ	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_sconj_ratio	share of sentences ending in a SCONJ	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_num_ratio	share of sentences ending in a NUM	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_aux_ratio	share of sentences ending in a AUX	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_pron_ratio	share of sentences ending in a PRON	sentences ending in that tag / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)
sentfinal_other_ratio	share of sentences ending in any other tag	sentences ending in a tag outside the 13 / sentences	at least 1 sentence with a word	de Marneffe et al. (2021) Table 1 (UPOS tag set)

morphological — Morphological style (spaCy)

19 keys.

Key	Description	Formula	Requires	Source
surface_per_lemma	distinct forms per lemma	distinct (lemma, form) pairs / distinct lemmas, lowercased	at least 1 word	de Marneffe et al. (2021) Table 2 (universal morphological features)
tense_past_ratio	share of words tagged Tense=Past	words with Tense=X / words with any Tense	at least 1 word with a Tense tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
tense_pres_ratio	share of words tagged Tense=Pres	words with Tense=X / words with any Tense	at least 1 word with a Tense tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
tense_fut_ratio	share of words tagged Tense=Fut	words with Tense=X / words with any Tense	at least 1 word with a Tense tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
aspect_perf_ratio	share of words tagged Aspect=Perf	words with Aspect=X / words with any Aspect	at least 1 word with an Aspect tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
aspect_imp_ratio	share of words tagged Aspect=Imp	words with Aspect=X / words with any Aspect	at least 1 word with an Aspect tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
aspect_prog_ratio	share of words tagged Aspect=Prog	words with Aspect=X / words with any Aspect	at least 1 word with an Aspect tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
case_nom_ratio	share of words tagged Case=Nom	words with Case=X / words with any Case	at least 1 word with a Case tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
case_acc_ratio	share of words tagged Case=Acc	words with Case=X / words with any Case	at least 1 word with a Case tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
case_dat_ratio	share of words tagged Case=Dat	words with Case=X / words with any Case	at least 1 word with a Case tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
case_loc_ratio	share of words tagged Case=Loc	words with Case=X / words with any Case	at least 1 word with a Case tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
case_abl_ratio	share of words tagged Case=Abl	words with Case=X / words with any Case	at least 1 word with a Case tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
case_gen_ratio	share of words tagged Case=Gen	words with Case=X / words with any Case	at least 1 word with a Case tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
person_1_ratio	share of words tagged Person=1	words with Person=X / words with any Person	at least 1 word with a Person tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
person_2_ratio	share of words tagged Person=2	words with Person=X / words with any Person	at least 1 word with a Person tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
person_3_ratio	share of words tagged Person=3	words with Person=X / words with any Person	at least 1 word with a Person tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
number_sing_ratio	share of words tagged Number=Sing	words with Number=X / words with any Number	at least 1 word with a Number tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
number_plur_ratio	share of words tagged Number=Plur	words with Number=X / words with any Number	at least 1 word with a Number tag	de Marneffe et al. (2021) Table 2 (universal morphological features)
voice_pass_ratio	share of passive verbs	VERB with Voice=Pass / VERB	at least 1 verb	de Marneffe et al. (2021) Table 2 (universal morphological features)

morphological_zeyrek — Morphological style (Zeyrek, TR only)

23 keys.

Key	Description	Formula	Requires	Source
zeyrek_agglutination_depth	visible suffixes per word	visible suffixes / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_suffix_char_length_ratio	share of word letters in suffixes	suffix letters / word letters	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_suffix_bigram_entropy	variety of suffix sequences	Shannon entropy (nats) of within-word visible suffix pairs	at least 1 word with 2 visible suffixes	Shannon (1948) — the entropy formula; Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_derivational_suffix_ratio	share of derivational suffixes	derivational / visible suffixes	at least 1 visible suffix	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_verb_suffix_diversity	suffix variety on verbs	mean distinct visible suffix tags per verb_suffix_window-verb chunk	at least 50 verbs (verb_suffix_window)	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_tense_past_def_ratio	share of verbs in the definite past (-DI)	verbs whose last tense tag is X / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_tense_past_nar_ratio	share of verbs in the reported past (-mIş)	verbs whose last tense tag is X / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_tense_present_ratio	share of verbs in the present	verbs whose last tense tag is X / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_tense_future_ratio	share of verbs in the future (-AcAk)	verbs whose last tense tag is X / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_negation_ratio	share of negative verbs	verbs with Neg or Unable / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_passive_ratio	share of passive verbs	verbs with Pass / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_plural_ratio	share of plural nominals	words with A3pl on a non-verb part / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_case_acc_ratio	share of words in the accusative case	words with Acc … Ins / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_case_dat_ratio	share of words in the dative case	words with Acc … Ins / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_case_loc_ratio	share of words in the locative case	words with Acc … Ins / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_case_abl_ratio	share of words in the ablative case	words with Acc … Ins / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_case_gen_ratio	share of words in the genitive case	words with Acc … Ins / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_case_ins_ratio	share of words in the instrumental case	words with Acc … Ins / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_conditional_suffix_ratio	share of conditional verbs	verbs with Cond / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_causative_suffix_ratio	share of causative verbs	verbs with Caus / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_modal_possibility_ratio	share of ability verbs	verbs with Able or Unable / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_modal_necessity_ratio	share of necessity verbs	verbs with Neces / verbs	at least 1 verb	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)
zeyrek_question_particle_ratio	share of question particles	words with root Ques / analysed words	at least 1 analysed word	Zeyrek (a Python port of Zemberek's morphotactics); tag set from Akın & Akın (2007)

phonetic — Phonetic patterns

14 keys.

Key	Description	Formula	Requires	Source
vowel_ratio	share of vowels	vowels / alphabet letters	at least 1 alphabet letter	—
front_vowel_ratio	share of front vowels	front vowels / alphabet letters	at least 1 alphabet letter	Göksel & Kerslake (2005) ch. 2 (the vowel system, front/back)
back_vowel_ratio	share of back vowels	back vowels / alphabet letters	at least 1 alphabet letter	Göksel & Kerslake (2005) ch. 2 (the vowel system, front/back)
harmony_fronting_ratio	share of words obeying front/back vowel harmony (TR only)	words whose vowels are all front or all back / words with 2+ vowels	at least 1 word with 2 vowels	Göksel & Kerslake (2005) §3.1 (fronting harmony); exceptions §3.4 — the measure counts them as disharmonic
harmony_rounding_ratio	share of words obeying rounding vowel harmony (TR only)	words where every vowel after an unrounded one is unrounded and every vowel after a rounded one is close-rounded or open-unrounded / words with 2+ vowels	at least 1 word with 2 vowels	Göksel & Kerslake (2005) §3.1 (rounding harmony); strictly a suffix phenomenon, measured here as a whole-word pattern
syllable_count	number of syllables	sum of syllables over syllabifiable words	non-empty text (0 when it has no syllabifiable words)	Kincaid et al. (1975) p.38, "Instructions for Recalculated Flesch Formula", step 3 "Count the number of syllables"
syllable_mean	mean syllables per word	mean syllables per syllabifiable word	at least 1 syllabifiable word	Flesch (1948) Formula A, wl; unit there = syllables per 100 words, here per word
syllable_1_ratio	share of words with 1 syllable	words with 1 syllable / syllabifiable words	at least 1 syllabifiable word	Bezirci & Yılmaz (2010) Table 1-c
syllable_2_ratio	share of words with 2 syllables	words with 2 syllables / syllabifiable words	at least 1 syllabifiable word	Bezirci & Yılmaz (2010) Table 1-c
syllable_3_ratio	share of words with 3 syllables	words with 3 syllables / syllabifiable words	at least 1 syllabifiable word	Bezirci & Yılmaz (2010) Table 1-c
syllable_4_ratio	share of words with 4 syllables	words with 4 syllables / syllabifiable words	at least 1 syllabifiable word	Bezirci & Yılmaz (2010) Table 1-c
syllable_5_ratio	share of words with 5 syllables	words with 5 syllables / syllabifiable words	at least 1 syllabifiable word	Bezirci & Yılmaz (2010) Table 1-c
syllable_6plus_ratio	share of words with 6 or more syllables	words with 6 or more syllables / syllabifiable words	at least 1 syllabifiable word	Bezirci & Yılmaz (2010) Table 1-c
sent_syllable_mean	syllables per sentence	mean syllables per sentence	at least 1 sentence with a syllabifiable word	—

readability — Readability scores

11 keys.

Key	Description	Formula	Requires	Source
bezirci_yilmaz	Bezirci-Yılmaz; higher = harder	sqrt(words/sentence * (0.84 H3 + 1.5 H4 + 3.5 H5 + 26.25 H6)), Hk per sentence	at least 1 syllabifiable word	Bezirci & Yılmaz (2010) p.371, eq. (9)
atesman	Ateşman; higher = easier	198.825 - 40.175 * syllables/word - 2.610 * words/sentence	at least 1 syllabifiable word	Ateşman (1997) p.74, eq. (2)
cetinkaya_uzun	Çetinkaya-Uzun; higher = easier	118.823 - 25.987 * syllables/word - 0.971 * words/sentence; ": ( )" end sentences	at least 1 syllabifiable word	Çetinkaya (2010) p.85; counting rules p.93
flesch_reading_ease	Flesch Reading Ease (EN)	206.835 - 1.015 * words/sentence - 84.6 * syllables/word	at least 1 syllabifiable word	Flesch (1948) Formula A; coefficient .846, unit = syllables per 100 words
flesch_kincaid_grade	Flesch-Kincaid grade (EN)	0.39 * words/sentence + 11.8 * syllables/word - 15.59	at least 1 syllabifiable word	Kincaid et al. (1975) p.14, Table 3, "New"
smog	SMOG grade (EN)	3.1291 + 1.0430 * sqrt(polysyllables * 30 / sentences)	at least 30 sentences	McLaughlin (1969) p.643, Table 1, eq. (d); p = polysyllabic words in a 30-sentence sample
ari	Automated Readability Index	4.71 * strokes/word + 0.5 * words/sentence - 21.43	at least 1 word	Smith & Senter (1967) p.8, AMRL-TR-66-220; reproduced verbatim in Kincaid et al. (1975) p.14, Table 3 ("Old")
coleman_liau	Coleman-Liau index	0.0588 * letters per 100 words - 0.296 * sentences per 100 words - 15.8	at least 1 word	Coleman & Liau (1975) p.284; the formula is a composition of two equations, it does not appear in this form in the article
lix	Björnsson's LIX	words/sentence + 100 * long words/words, long = 7+ letters	at least 1 word	Björnsson (1968), as cited in Anderson (1983) p.490; long word = 7+ letters
polysyllabic_word_ratio	share of 3+ syllable words (EN)	3+ syllable words / syllabifiable words	at least 1 syllabifiable word	McLaughlin (1969) p.641; polysyllabic = 3+ syllables — the ratio form of SMOG's input, not the source's own measure
long_word_ratio	share of 7+ letter words	long words / words	at least 1 word	Anderson (1983); long word = 7+ letters

punctuation — Punctuation & digits

20 keys.

Key	Description	Formula	Requires	Source
char_count	number of characters	characters, whitespace and line breaks included	non-empty text	de Vel (2000) Table 2, attribute 8 "Number of characters"; Zheng et al. (2006) Table 3, p.385, no. 1 "Total number of characters (C)"
digit_ratio	share of digit characters	digits / characters	non-empty text	de Vel et al. (2001) Table 2, p.60 "Total number of digit characters in words/C"; here digits anywhere in the text
punct_count	number of punctuation marks	marks of the 10 mark types	non-empty text	de Vel et al. (2001) Table 2, p.60 "Total number of punctuations/C", here not divided by C
punct_comma_ratio	share of comma marks among all marks	marks of this type / all marks	at least 1 punctuation mark	Zheng et al. (2006) Table 3, p.385, no. 88-95 (frequencies of eight marks, this one among them); here the share among all marks
punct_period_ratio	share of full stop marks among all marks	marks of this type / all marks	at least 1 punctuation mark	Zheng et al. (2006) Table 3, p.385, no. 88-95 (frequencies of eight marks, this one among them); here the share among all marks
punct_semicolon_ratio	share of semicolon marks among all marks	marks of this type / all marks	at least 1 punctuation mark	Zheng et al. (2006) Table 3, p.385, no. 88-95 (frequencies of eight marks, this one among them); here the share among all marks
punct_exclamation_ratio	share of exclamation marks among all marks	marks of this type / all marks	at least 1 punctuation mark	Zheng et al. (2006) Table 3, p.385, no. 88-95 (frequencies of eight marks, this one among them); here the share among all marks
punct_colon_ratio	share of colon marks among all marks	marks of this type / all marks	at least 1 punctuation mark	Zheng et al. (2006) Table 3, p.385, no. 88-95 (frequencies of eight marks, this one among them); here the share among all marks
punct_dash_ratio	share of hyphen or dash marks among all marks	marks of this type / all marks	at least 1 punctuation mark	—
punct_ellipsis_ratio	share of ellipsis marks among all marks	marks of this type / all marks	at least 1 punctuation mark	—
punct_paren_ratio	share of parenthesis marks among all marks	marks of this type / all marks	at least 1 punctuation mark	—
punct_quote_ratio	share of quotation marks among all marks	marks of this type / all marks	at least 1 punctuation mark	Zheng et al. (2006) Table 3, p.385, no. 88-95 (frequencies of eight marks, this one among them); here the share among all marks
punct_question_ratio	share of question marks among all marks	marks of this type / all marks	at least 1 punctuation mark	Zheng et al. (2006) Table 3, p.385, no. 88-95 (frequencies of eight marks, this one among them); here the share among all marks
punct_char_ratio	share of punctuation marks among characters	marks / characters	non-empty text	de Vel et al. (2001) Table 2, p.60 "Total number of punctuations/C"
punct_entropy	variety of punctuation types	Shannon entropy (nats) of the 10 mark types	at least 1 punctuation mark	Shannon (1948) — the entropy formula; applying it to the distribution of punctuation types is this library's own decision
consecutive_punct_ratio	share of marks directly next to another mark	adjacent marks / marks	at least 1 punctuation mark	—
whitespace_ratio	share of whitespace characters	whitespace / characters	non-empty text	de Vel et al. (2001) Table 2, p.60 "Total number of white-space characters/C"
punct_variety	number of punctuation types used (0–10)	distinct mark types	non-empty text	—
uppercase_ratio	share of capitalised words	words whose first letter is upper case / words with a letter	at least 1 word with a letter	—
all_caps_word_ratio	share of all-caps words	words with 2+ letters, all upper case / words with a letter	at least 1 word with a letter	—

chars — Character frequency vector [dynamic: one key per letter — TR 29, EN 26]

Dynamic group: keys are generated with the prefix char_. The description, formula and requirement below are stated at the group level, not per key.

Key	Description	Formula	Requires	Source
char_…	share of that letter among alphabet letters	letter count / alphabet letters	at least 1 alphabet letter	Zheng et al. (2006) Table 3, p.385, no. 7-32 "Frequency of letters (26 features)", A-Z; here each letter of the language's alphabet (Turkish 29, English 26) as a share of all its letters

custom_ngrams — User-defined n-gram counts [dynamic: ngram_{...}_count]

Dynamic group: keys are generated with the prefix ngram_. The description, formula and requirement below are stated at the group level, not per key.

Created only when you pass custom_ngrams to analyze().

Bibliography

56 works. Every citation above names at least one of these verbatim, and every entry here is named by at least one citation — both directions are tested.

Akın & Akın (2007)

Akın, A. A., & Akın, M. D. (2007). Zemberek, an open source NLP framework for Turkic Languages. 8 pp. Source code: github.com/ahmetaa/zemberek-nlp. (The document does not state a place of publication.)

Anderson (1983)

Anderson, J. (1983). Lix and Rix: Variations on a little-known readability index. Journal of Reading, 26(6), 490–496. JSTOR 40031755.

Ateşman (1997)

Ateşman, E. (1997). Türkçede okunabilirliğin ölçülmesi. Dil Dergisi, 58, 71–74. Ankara Üniversitesi TÖMER. ISSN 1300-3542.

Bestgen (2023)

Bestgen, Y. (2023). Measuring lexical diversity in texts: The twofold length problem. arXiv:2307.04626. Published version: Language Learning, 74(3), 638–671 (2024), DOI 10.1111/lang.12630 — the preprint was used here.

Bezirci & Yılmaz (2010)

Bezirci, B., & Yılmaz, A. E. (2010). Türkçe için yeni bir okunabilirlik ölçütü önerisi. SIU2010 — IEEE 18. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Diyarbakır, 368–371.

Björnsson (1968)

Björnsson, C. H. (1968). Läsbarhet. Stockholm: Bokförlaget Liber. (Book.) The record was verified from three secondary reference lists: Anderson (1983), Çetinkaya (2010), Falkenjack et al. (2013). The primary source could not be obtained.

Brunet (1978)

Brunet, E. (1978). Vocabulaire de Jean Giraudoux: structure et évolution. Genève: Slatkine. (Book.) The record was verified from the reference list of Popescu, Čech & Altmann (2011). The primary source could not be obtained.

Carroll (1964)

Carroll, J. B. (1964). Language and Thought. Englewood Cliffs, NJ: Prentice-Hall. The record was verified from the reference list of Torruella & Capsada (2013). The primary source could not be obtained.

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Coleman, M., & Liau, T. L. (1975). A computer readability formula designed for machine scoring. Journal of Applied Psychology, 60(2), 283–284. DOI 10.1037/h0076540

Covington & McFall (2010)

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Daller, M. (2010). Guiraud's Index. BAAL 2010, Aberdeen. (Presentation.)

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Deutsch, T., Jasbi, M., & Shieber, S. (2020). Linguistic features for readability assessment. Proceedings of the 15th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), 1–17. DOI 10.18653/v1/2020.bea-1.1

Dugast (1978)

Dugast, D. (1978). Sur quoi se fonde la notion d'étendue théoretique du vocabulaire? Le Français Moderne, 46(1), 25–32. The record was verified from four secondary reference lists: Malvern et al. (2004), McCarthy & Jarvis (2010), Šišková (2012) and an authorship-attribution review. The primary source could not be obtained. The literature often cites Dugast (1978, 1979) together; 1979 is a separate work (Vocabulaire et stylistique I, Travaux de linguistique quantitative 8, Genève: Slatkine-Champion) and is not used here.

Flesch (1948)

Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221–233. DOI 10.1037/h0057532

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Guiraud, P. (1954). Les Caractères Statistiques du Vocabulaire. Essai de méthodologie. Paris: Presses Universitaires de France.

Göksel & Kerslake (2005)

Göksel, A., & Kerslake, C. (2005). Turkish: A Comprehensive Grammar. London & New York: Routledge. 535 pp. ISBN 0-415-11494-2 (pbk), 0-415-21761-X (hbk).

Heaps (1978)

Heaps, H. S. (1978). Information Retrieval: Computational and Theoretical Aspects. New York: Academic Press.

Herdan (1955)

Herdan, G. (1955). A new derivation and interpretation of Yule's characteristic K. Zeitschrift für Angewandte Mathematik und Physik, 6. The record was verified from the reference list of Tweedie & Baayen (1998). The primary source could not be obtained.

Herdan (1960/1964)

Herdan, G. (1960). Type-Token Mathematics. The Hague: Mouton. / Herdan, G. (1964). Quantitative Linguistics. London: Butterworths.

Honoré (1979)

Honoré, A. (1979). Some simple measures of richness of vocabulary. Association for Literary and Linguistic Computing Bulletin, 7(2), 172–177. The record was verified from the reference list of Tweedie & Baayen (1998). The primary source could not be obtained.

Jing & Liu (2015)

Jing, Y., & Liu, H. (2015). Mean hierarchical distance: Augmenting mean dependency distance. Proceedings of Depling 2015, Uppsala, 161–170.

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Johnson, W. (1944). Studies in language behavior: I. A program of research. Psychological Monographs, 56(2), 1–15. DOI 10.1037/h0093508

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Kincaid, J. P., Fishburne, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas for Navy enlisted personnel. Research Branch Report 8-75. Millington, TN: Naval Air Station Memphis. DOI 10.21236/ADA006655

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Liu, H. (2008). Dependency distance as a metric of language comprehension difficulty. Journal of Cognitive Science, 9(2), 159–191. DOI 10.17791/jcs.2008.9.2.159

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Lu, X. (2012). The relationship of lexical richness to the quality of ESL learners' oral narratives. The Modern Language Journal, 96(2), 190–208. DOI 10.1111/j.1540-4781.2011.01232.x

Maas (1972)

Maas, H.-D. (1972). Zusammenhang zwischen Wortschatzumfang und Länge eines Textes. Zeitschrift für Literaturwissenschaft und Linguistik, 8, 73–79. The record was verified from the reference list of Tweedie & Baayen (1998). The primary source could not be obtained.

Malvern et al. (2004)

Malvern, D., Richards, B., Chipere, N., & Durán, P. (2004). Lexical Diversity and Language Development: Quantification and Assessment. Basingstoke: Palgrave Macmillan. ISBN 978-1-4039-0232-0. DOI 10.1057/9780230511804.

Manning et al. (2008)

Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. (The file in the archive is the 2009 online edition.) DOI 10.1017/CBO9780511809071

McCarthy & Jarvis (2007)

McCarthy, P. M., & Jarvis, S. (2007). vocd: A theoretical and empirical evaluation. Language Testing, 24(4), 459–488. DOI 10.1177/0265532207080767

McCarthy & Jarvis (2010)

McCarthy, P. M., & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381–392. DOI 10.3758/BRM.42.2.381

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Popescu, Čech & Altmann (2011)

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QUITA

Kubát, M., Matlach, V., & Čech, R. (2014). QUITA — Quantitative Index Text Analyzer. Lüdenscheid: RAM-Verlag. ISBN 978-3-942303-28-6.

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This library's threshold calibration

This library's own measurement, not a published source. short_sent_threshold and long_sent_threshold were derived from the 15th and 85th percentiles of the sentence-length distribution of newspaper columns under the default sentence and word rules: TR 162 columnists / 4,321 articles / 197,990 sentences, EN 30 columnists / 1,485 articles / 52,745 sentences. Method and raw percentile table: docs/threshold-calibration.md.

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Torruella, J., & Capsada, R. (2013). Lexical statistics and tipological structures: A measure of lexical richness. Procedia - Social and Behavioral Sciences, 95, 447–454. DOI 10.1016/j.sbspro.2013.10.668

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Yule (1944)

Yule, G. U. (1944). The Statistical Study of Literary Vocabulary. Cambridge University Press.

Zeyrek

Zeyrek — a Python port of the Zemberek morphological analyser. github.com/obulat/zeyrek

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Zheng, R., Li, J., Chen, H., & Huang, Z. (2006). A framework for authorship identification of online messages: Writing-style features and classification techniques. Journal of the American Society for Information Science and Technology, 57(3), 378–393. DOI 10.1002/asi.20316

de Marneffe et al. (2021)

de Marneffe, M.-C., Manning, C. D., Nivre, J., & Zeman, D. (2021). Universal Dependencies. Computational Linguistics, 47(2), 255–308. DOI 10.1162/COLI_a_00402

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de Vel, O. (2000). Mining e-mail authorship. In KDD-2000 Workshop on Text Mining, Boston, August 20, 2000.

de Vel et al. (2001)

de Vel, O., Anderson, A., Corney, M., & Mohay, G. (2001). Mining e-mail content for author identification forensics. ACM SIGMOD Record, 30(4), 55–64. DOI 10.1145/604264.604272

van Heuven et al. (2014)

van Heuven, W. J. B., Mandera, P., Keuleers, E., & Brysbaert, M. (2014). SUBTLEX-UK: A new and improved word frequency database for British English. Quarterly Journal of Experimental Psychology, 67(6), 1176–1190. DOI 10.1080/17470218.2013.850521

Çetinkaya (2010)

Çetinkaya, G. (2010). Türkçe metinlerin okunabilirlik düzeylerinin tanımlanması ve sınıflandırılması [Unpublished doctoral dissertation]. Ankara Üniversitesi, Sosyal Bilimler Enstitüsü. Advisor: Leylâ Uzun. 252 pp. hdl:20.500.12812/519962