# Typology meets statistical modeling: The German gender system

Sebastian Fedden, Matías Guzmán Naranjo, Greville G Corbett

<figure><img src="image_1.png" /></figure>

## To cite this version:

Sebastian Fedden, Matías Guzmán Naranjo, Greville G Corbett. Typology meets statistical modeling: The German gender system. Language, 2025, 101 (2), pp.251-290. 10.1353/lan.2025.a962898. hal-05132373

HAL Id: hal-05132373

https://hal.science/hal-05132373v1

Submitted on 27 Jun 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

PROJECT MUSE®

# Typology meets statistical modeling: The German gender system

Sebastian Fedden, Matías Guzmán Naranjo, Greville G. Corbett

Language, Volume 101, Number 2, June 2025, pp. 251-290 (Article)

Published by Linguistic Society of America

DOI: https://doi.org/10.1353/lan.2025.a962898

LANGUAGE

A JOURNAL OF THE LINGUISTIC SOCIETY OF AMERICA

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

For additional information about this article

https://muse.jhu.edu/article/962898
TYPOLOGY MEETS STATISTICAL MODELING: THE GERMAN
GENDER SYSTEM

SEBASTIAN FEDDEN

Université Sorbonne Nouvelle/
LACITO, Ludwig-Maximilians-
Universität München, and
University of Surrey

MATÍAS GUZMÁN NARANJO

Universität Freiburg

GREVILLE G. CORBETT

University of Surrey and
Max Planck Institute
for Evolutionary
Anthropology, Leipzig

The German gender system is known for its complexity, and there is a persistent misconception that it is largely arbitrary, and hence a challenge for the typology of gender systems. In response, we construct a database of more than 30,000 German nouns and show that a BOOSTING TREE model achieves a predictive success of 96%. Even more surprising, the model performs at 87% when trained on just the 100 most frequent nouns. We thus demonstrate that the complex German system fits into a typologically well-known scheme, being a combination of semantic and formal assignment principles. In addition to our success with the specific problem, we show the value of statistical modeling for typologists and reflect on what exactly we can learn from these techniques.*

Keywords: assignment rules, statistical modeling, gender, German, morphosyntax, typology

# 1. INTRODUCTION.
In recent years, linguistic typology has increasingly profited from statistical modeling; the hope is to discover patterns in large data sets more quickly and more accurately than would be possible for a human researcher. A linguistic system that could benefit from such an approach is German gender, which from a typological perspective is unusually complex.

## 1.1. GERMAN AS A TYPOLOGICAL GEM.
German gender has complex gender assignment principles, as western European languages do in general (for discussion see Enger 2009 on Norwegian, Audring 2014 on Dutch, Audring 2023 on Germanic in general, and Bonami, Guzmán Naranjo, & Tribout 2019 on French, among others). By gender assignment principles we mean the generalizations that allow the gender of a noun to be inferred from other necessary information, that is, its meaning, its phonological shape, and its morphological structure and inflection. A recent compilation of factors (Corteen 2018:58) lists no fewer than 378 semantic and formal assignment principles mentioned in the literature on German gender assignment, some in conflict with each other. And yet, of course, children learn the system (Mills 1986), and, as in other gender

<footnote>* Versions of this paper were read at the MultiGender Workshop ‘A multilingual approach to grammatical gender’, Centre for Advanced Study, Oslo 2020, at the 53rd annual meeting of the Societas Linguistica Europaea, Bucharest (online) 2020, at the 54th annual meeting of the Societas Linguistica Europaea, Athens (online) 2021, and at the International Symposium of Morphology (ISMo), Paris (online) 2021. We thank members of those audiences for their comments. We are grateful to colleagues who offered helpful suggestions, and to those who read and commented on drafts of the paper: Jenny Audring, Matthew Baerman, Laura Becker, Sacha Beniamine, Dunstan Brown, Johannes Dellert, Hans-Olav Enger, Sebastian Kürschner, Barbara Schlücker, and Anna Thornton. We thank Olivier Bonami for suggesting this collaboration, and Lisa Mack and Penny Everson for their help with the preparation of the manuscript. Our special thanks go to the editors of *Language* Andries Coetzee and John Beavers, the co-editor Shelome Gooden, associate editor Titus von der Malsburg, and the two referees, Harald Baayen and an anonymous referee, for their sustained constructive engagement with the paper. Financial support from the ESRC (grant ES/R00837X/1 ‘Optimal categorization: The origin and nature of gender from a psycholinguistic perspective’), from the Emmy Noether project (grant 504155622 ‘Bayesian modelling of spatial typology’), and from the Centre for Advanced Study, Oslo, is gratefully acknowledged. Ce travail a bénéficié d’une aide de l’Etat gérée par l’Agence Nationale de la Recherche au titre du programme ‘Investissements d’Avenir’ portant la référence ANR-10-LABX-0083. Il contribue à l’IdEx Université de Paris - ANR-18-IDEX-0001.</footnote>
languages, intuitions about gender assignment are remarkably consistent across speakers and they make few or no errors (Corbett 1991:7, Corteen 2018:5, and references there).

The three gender values of German, masculine, feminine, and neuter, are illustrated in 1.<fnref n="1" />

(1) a. der Film
DEF.NOM.M.SG film(M)[NOM.SG]
'the film'

b. die Symphonie
DEF.NOM.F.SG symphony(F)[NOM.SG]
'the symphony'

c. das Buch
DEF.NOM.N.SG book(N)[NOM.SG]
'the book'

This appears easy: many systems have more gender values than German. Furthermore, the basic semantic assignment principles are relatively straightforward, as we will see. But the system is a tangle of interacting semantic, morphological, and phonological assignment principles. This typological gem keeps on offering partial results and then posing new questions.

To make progress we interrogate a database of more than 30,000 German nouns, based on the CELEX database (Baayen, Piepenbrock, & Gulikers 1995), fully annotated for phonological shape, morphological structure, inflection class, and semantics; we augment this by taking frequency information from the DECOW16b corpus (Schäfer & Bildhauer 2012, Schäfer 2015). We then induce the gender assignment principles from our data set of German nouns using a BOOSTING TREE model (Chen et al. 2015).

The article is structured as follows. We first establish the theoretical and typological frame for our study (§2), and then present the typology of gender assignment (§3), introducing semantic assignment principles and formal assignment principles (derivation, inflection, phonological shape). This is followed in §4 by an overview of the essentials of gender in German, both in terms of realization and in terms of assignment. Section 5 introduces the new data and new methods used here, and we discuss the overall results in §6, highlighting the effects of frequency and the issue of nested generalizations, where combinations of criteria make a prediction that neither of the individual criteria allows individually. Finally, in §7 we offer our conclusions, drawing together what we have learned about typology and statistical modeling.

2. GERMAN GENDER ASSIGNMENT: STATE OF THE ART. Gender assignment principles are generalizations over lexical knowledge. The relevant literature was already substantial in the mid-1980s (Corbett 1986 lists over seventy-five items); see Corteen 2018 for a substantially larger recent listing. In our discussion, we confine ourselves to key references.

2.1. SPEAKER COMPETENCE. The evidence by which speakers learn the gender of nouns is provided by their agreement patterns (illustrated in part in 1). In order to use a German noun, speakers require its gender value, along with the other information

<footnote n="1">¹ We follow the Leipzig glossing rules (https://www.eva.mpg.de/lingua/resources/glossing-rules.php); [ ] is for nonovert elements (as when information is inferred from the use of the bare stem); ( ) is for inherent feature values; bold is used as a flag to draw attention to relevant characteristics of examples. Abbreviations comprise the Leipzig glosses and additions: 3: third person, DEF: definite, DERIV: derivation, F: feminine, IC: inflection class, INF: infinitive, INFL: inflection, M: masculine, N: neuter, NOM: nominative, PHON: phonology, PL: plural, PRS: present, SBJ: subject, SEM: semantics, SG: singular.</footnote>
forming its lexical representation, which includes lexical semantics, information about
morphological structure and inflection, and its phonological form. Some of these spec-
ifications are mutually reinforcing: thus, the phonological form of a noun may offer
predictions about how it inflects. Gender stands out, in that when systems are investi-
gated in detail, gender proves to be predictable to a high degree from other lexical infor-
mation. Hence while gender might be counted as an additional ‘cost’ in lexical storage,
compared with languages with no gender, this is only apparent. This does not imply that
speakers compute the gender value each time they use a noun; it seems highly likely
that the gender of common items is stored. Yet, the gender assignment principles retain
a role, in that such generalizations over the lexicon serve to maintain the integrity of
lexical information. There are also situations in which speakers need to assign a gender
value to new nouns, such as borrowings or nonce words in psycholinguistic experimen-
tation, or when they hear a new noun in a context that does not provide gender cues
(e.g. bare plurals). We assume that speakers assign a gender value based on the same
generalizations that already structure their lexicon; in other words, such novel assign-
ment is a window into the normal system of gender assignment. As we shall see, these
gender assignment principles are generalizations based on semantic, morphological,
and phonological information.

2.2. **ACQUISITION.** The essential work on the acquisition of German gender is Mills’s fine study (1986); since then, there has been research on demanding aspects of the problem. There are helpful surveys of the literature, as in Binanzer 2017:43–101, Walter, Fritzsche, & Höhle 2021:747–49, and Kupisch, Geiss, et al. 2022:4–7. These demonstrate both interesting progress and, conversely, the large gaps in our understanding of the basic issue of how a monolingual German-speaking child comes to master the complex gender system so effectively. The importance of phonological cues is well documented in these papers, and also in Szagun et al. 2007, but there is much more to be understood about the way in which other factors come to the fore (Kupisch, Mitrofanova, & Westergaard 2022). For gender congruency effects, see Sá-Leite et al. 2022 and references there.

German gender is complex both in terms of the number of assignment principles
and in terms of the interaction of these principles. Despite this, our model is highly
successful for gender assignment. What we mean is that it accounts for how well L1
speakers deal with the system. L2 speakers, by contrast, often fail at gender assignment
in German and other languages because they lack ability in statistical learning in gen-
eral. We know that L2 speakers also struggle with other types of classification problem
like inflection class assignment or selecting the correct derivation process in cases of
competition (Dykstra-Pruim 2003, Callies 2015, Schuhmann & Smith 2022). For all of
these problems, it has been shown that they are easily solvable by L1 speakers and that
there are strong statistical tendencies, much as for gender.

2.3. **GENDER DISTRIBUTION AND FREQUENCY.** Given the complexity of the system, an important step is to put numbers to predictions. Gender distribution is a nontrivial issue since it crucially depends on the sample (see Opitz & Pechmann 2016:237). The overall distribution in our database of 30,576 nouns is given in Table 1 (in addition, 313 hybrids were set aside because they do not have a consistent gender value;<fnref n="2" /> see Corbett 2012:85–88 and Corbett & Fedden 2016:517–19).

<footnote n="2">² The best-known example is *Mädchen* 'girl'. This is a hybrid since its agreement pattern varies depending on the target. In the noun phrase agreement has to be neuter, and the relative pronoun similarly has to</footnote>
<table><caption>TABLE 1. Overall distribution of nouns across genders (based on a set of 30,576 nouns; hybrids not included).</caption><thead><tr><th></th><th>PROPORTIONS</th><th>ABSOLUTE NUMBERS</th></tr></thead><tbody><tr><td>MASCULINE</td><td>36%</td><td>10,866</td></tr><tr><td>FEMININE</td><td>45%</td><td>13,766</td></tr><tr><td>NEUTER</td><td>19%</td><td>5,944</td></tr></tbody></table>

When evaluating predictors, we compare their accuracy to the overall distribution of nouns over the gender values. This allows us to ascertain that a predictor is contributing something. So a predictor is of value only if its accuracy is better than the baseline of 45% of feminine nouns.<fnref n="3" />

Corpus frequency turns out to be important as well. The gender distribution for the most frequent nouns is different from the distribution we obtain if we add less frequent nouns. Thus, we take frequency into account when looking at the distribution of nouns across genders. Further, the most frequent items are the most exceptional ones (Bybee 1985, Wu et al. 2019). The clear hypothesis from this well-established fact is that they are therefore less good as input for learning gender. Our results disprove this hypothesis, showing that our model achieves 87% accuracy when trained on just the 100 most frequent nouns.

2.4. MODELING. There has been a good deal of work on gender assignment in German, notably by Klaus-Michael Köpcke and David Zubin on the role of semantics and phonology in gender assignment (Köpcke 1982, Köpcke & Zubin 1983, 1984, 1996, 2009, Zubin & Köpcke 1984); this fruitful line of enquiry, which began in the 1980s, has demonstrated clear regularities in the assignment of gender to German nouns. Their assignment rules are strictly ordered (and numbered): semantic rules apply first, then morphological rules, which they restrict to the way nouns inflect for plural, and finally different types of phonological rule, which are carefully ordered so that the most predictively successful rules apply first (Köpcke 1982:109–16). Phonological rules refer to onsets (e.g. /kn_/) , codas (e.g. /_(C)ft/), combinations of nucleus and coda (e.g. /_ur/), or phonotactic structure (e.g. /CC_CC/).

Köpcke (1982) points out that fewer rules would have been sufficient for efficient gender assignment (his higher-number rules typically do little work or do not come into play at all), but he says that rather than trying to develop ‘ein möglichst effektives Zuweisungsprogramm’ [a maximally efficient assignment schema], he is concerned about the ‘Auffindung möglicherweise zugrunde liegender Lern- und Speicherungshilfen bei der Genuszuweisung durch den native speaker des Deutschen’ [identification of possibly underlying learning and storage aids in gender assignment by the native speaker of German—our translation] (p. 113). Köpcke conceives of gender assignment principles as mutually reinforcing learning and memory aids.

be neuter, but an anaphoric pronoun can be either neuter es ‘it’ (syntactic agreement) or feminine sie ‘she’ (semantic agreement). The conditions on the choice are intriguing; for instance, the age of the referent is important, as Braun and Haig (2010) demonstrate (see also Köpcke, Panther, & Zubin 2010 and Birkenes, Chroni, & Fleischer 2014). For impressive data on the diachrony of German hybrids, see Birkenes et al. 2014.

<footnote n="3">³ If a predictor is restricted to a domain of application, its accuracy should be evaluated based on that domain. For example, the accuracy of a semantic predictor like ‘alcoholic beverages are masculine’ should be calculated as the number of masculine alcoholic beverages divided by the total number of nouns that denote alcoholic beverages.</footnote>
Köpcke showed clearly that German gender assignment is not arbitrary, and this was a great contribution—going against the prevailing wisdom of Bloomfield (1933:280) and others (see Corteen 2018:2–3 for more recent suggestions that it is arbitrary). However, there are some problems with his approach, which we now address.

First, there is the general issue that the overall accuracy of Köpcke’s rules (semantic, plural, and phonological together) is rather low in relation to his sample. In an efficient assignment system, his overall success rate of 65.4% for all genders (Köpcke 1982:114) could have been accomplished with a single rule stating that all German nouns are masculine. This would yield a success rate of 64.12%, given Köpcke’s sample, which is based on DUDEN (a total of 1,466 monosyllabic nouns: 940 masculine (64.12%), 205 feminine (13.98%), 321 neuter (21.9%)). The success rate increases considerably, to 89.2%, if we allow equivocal situations: that is, if we allow a rule to predict not a single gender, but either of two genders, excluding the third. But again, we obtain almost the same accuracy (86%) if we simply just predict that German nouns are either masculine or neuter.

Second, there are specific issues with the way the phonological rules are implemented in Köpcke 1982, which makes them less apt for use with our larger sample. We take up these points in more detail in §4.3 on phonological assignment principles.

Despite this analytical progress and the considerable typological interest of the German system, no attempt has been made to analyze the system as a whole, as has been done, for example, for other languages that rely on a combination of semantic and formal assignment rules (see e.g. Corbett 1991:34–43, 57–62 for Russian and French). Studies on German are usually restricted: either they are based on a small sample of nouns, for example, 1,466 nouns in Köpcke 1982 and 592 nouns in Corteen 2018, or they are confined to a subset of gender predictors. An example of the latter type is a recent study by Basirat et al. (2021), who use a bespoke data set constructed for the purpose of their study, but do not take morphological information (derivation, inflection class) into account. With a training set of tens of thousands of nouns, they reach an accuracy of 88% using phonology, and 87% with a model that combines phonology and semantics. Other studies look at two predictors; for example, Nastase and Popescu (2009) use phonology and morphology for German and Romanian but achieved accuracies of only 72% for German and 78% for Romanian. By contrast, Williams et al. (2019) look solely at the semantic contribution of gender assignment, for a small subset of animate nouns across eighteen languages, and find a clear semantic effect across all languages studied (see also Veeman 2020 for a discussion of contextualized embeddings). None of these restricted approaches gives us a holistic account of gender assignment in German. It is evident that we need better data and better methods; overcoming these limitations is the challenge we take up here.

Outside of German, there has been considerable computational modeling work on gender assignment—too many studies for us to provide a comprehensive list. Well-known examples include Matthews (2010), who looks at French gender assignment using a memory-based learner (TiMBL) and finds that the final syllable segments are the best predictors of gender in French. The same memory-based framework is pursued by Durieux et al. (2000) in an investigation of multiple lexical categories, including gender, leading to comparable results, and Eddington (2002) uses similar techniques to explore gender assignment in Spanish.

**2.5. OUR METHOD.** We assume that assignment works by similarity; we therefore adopt a computational model that specifically relies on similarities. We represent the
phonology of nouns as their similarity to their nearest masculine, feminine, and neu-
ter neighbors, using a modified Levenshtein distance. For semantics we also use a
distance-based approach, calculating the semantic similarity between nouns based on
the hypothesis that words with similar meanings share similar neighbors in a corpus. We
compare this with lexical semantic information from our database (animacy, sex, etc.).
For morphology, we take the information on morphological structure and inflectional
pattern from the database. To induce the gender assignment principles from our data set
of German nouns we use a boosting tree model (Chen et al. 2015). We do not claim that
speakers actually perform boosting trees in their minds. Rather, we assume that speakers
have access to semantic, morphological, and phonological information, which they can
use to assign gender to nouns, but we do not make any assertions here as to how exactly
this is done. We discuss the method in detail in §5.

2.6. TYPOLOGY AND STATISTICAL MODELING. Statistical modeling is used for finding significant patterns in large data sets. While applying statistical modeling to a specific case, German gender, we show how typologists can benefit generally; we aim to raise awareness of the advantages and the challenges of this method.

First, the methods of statistical modeling are fast. Finding meaningful patterns in
gender assignment by hand in a large database of German nouns would take months (if
not years) of painstaking and error-prone work. Indeed, manually evaluating all interac-
tions of all the factors we consider in this article would be impossible with the number
of nouns (over 30,000) in our database. And since typologists are interested in crosslin-
guistic comparison, this time problem is compounded. Thus, statistical modeling allows
us to look at a fuller set of data.

Then, from the typologist’s perspective, statistical modeling is systematic and leads
to comparable results (Beniamine 2018, Guzmán Naranjo & Becker 2021). If we apply
a traditional approach to data sets from different languages, there is no guarantee of
comparability (Bonami & Beniamine 2021). As linguists we may make unconscious
decisions that affect the analysis. The computer does not. If the procedure is well
designed, we should obtain consistent and comparable results for different languages.
Given the availability of crosslinguistic corpora and databases, a number of studies have
taken advantage of computational and statistical techniques (for some recent examples,
see Guzmán Naranjo & Becker 2021, Levshina 2022, Talamo & Verkerk 2022, Sylvain
et al. 2023).

However, statistical modeling comes with its own challenges. The method can be eas-
ily misapplied and the results misinterpreted (Sönning & Werner 2021, Winter 2022).
There are pitfalls when using statistical techniques: a small error at any step of the
analysis will propagate and can have a significant impact on the results. As with any
method, care must be taken that it is a good fit to our research questions and the data
sets we work with.

Finally, the results of statistical modeling require linguistic analysis and linguistic
interpretation: the methods of statistical modeling are tools, like other tools, and the
results of a judicious vs. a heedless application of them will vary, like the sensible
vs. the careless use of a chainsaw. They require careful linguistic preparation. Our
computational methods are only as good as the linguistic analysis that supports them
(Janda 2019).

3. THE TYPOLOGY OF GENDER ASSIGNMENT. Despite its complexity, the German system fits the typology of gender assignment well, combining semantic and formal assignment
principles. To show this we outline the possible systems of gender assignment. The key
distinction is between semantic and formal gender assignment (Corbett 1991:7–62):

*   **SEMANTIC ASSIGNMENT**: information on the lexical semantics of a noun is sufficient to predict its gender for all or almost all nouns.

*   **FORMAL ASSIGNMENT** provides additional information, which can be:

*   morphological (inflectional or derivational);

*   phonological, based on the shape of the stem.

There are languages whose assignment systems rely exclusively (or almost exclusively) on semantic assignment principles, and languages with systems that complement semantic principles with formal principles. There are no systems that are exclusively formal. This is particularly interesting given that children tend to favor formal cues; see Culbertson et al. 2017 and Culbertson et al. 2019 for discussion, in the context of artificial language learning.

The Nakh-Dagestanian language Bagvalal, spoken in southwestern Dagestan by approximately 1,500 speakers, has a **SEMANTIC ASSIGNMENT SYSTEM**. In Bagvalal, agreement gives evidence for three gender values. Assignment is exclusively semantic: nouns denoting male humans are masculine, nouns denoting female humans are feminine, and all remaining nouns are neuter. Thus, neuter gender comprises all nonhumans, whether animate or inanimate.

(2) Bagvalal (Kibrik 2001:64–65)

a. waša w-iRi
boy(M) M.SG-stop
‘the boy stopped’

b. jaš j-iRi
girl(F) F.SG-stop
‘the girl stopped’

c. šama b-iRi
donkey(N) N.SG-stop
‘the donkey stopped’

Similar systems are prevalent in the Dravidian family, for example, Tamil (Asher 1985:36–37, Corbett 1991:8–9).

While gender assignment is achieved exclusively and straightforwardly by semantic principles in languages like Bagvalal and Tamil, the core semantic principles (female/ male or animate/inanimate) leave small numbers of nouns unaccounted for in other languages. These are languages with ‘predominantly semantic assignment’. A good example is Bininj Gun-wok (Mayali), as analyzed in detail in Evans et al. 2002 (see also Evans 2003). Taken together, languages with semantic assignment and predominantly semantic assignment account for around half of the languages with gender systems. In a sample of 257 languages in the *World atlas of language structures*, Corbett (2013) found that 112/257 (44%) of languages had a gender system, and of these, fifty-three (47%) had a semantic or predominantly semantic assignment system.<fnref n="4" />

Going further, we find other languages in which the core semantic principles leave a large semantic residue. Unlike languages such as Bagvalal, where nouns in the semantic residue (those not denoting humans) all make up a single gender value (the neuter), the

<footnote n="4">⁴ We are concerned here with core assignment principles. For minor semantic categories ('crazy rules'), see §5.1.</footnote>
semantic residue is distributed across the gender values in many languages. In this case, languages also make use of formal assignment rules, which relate to morphological or phonological properties of the noun, such as the way a noun inflects or the phonological shape of the stem. Formal assignment principles can generalize at different levels, which we illustrate briefly.

A good example of morphological assignment can be found in Russian. The semantic principles take precedence—that is, males are masculine and females are feminine; for the residue, the gender of a noun can be predicted from the inflection class to which it belongs (four main classes can be established). Thus, for example, *zakon* ‘law’ belongs to inflection class I and is masculine, and *kniga* ‘book’ belongs to inflection class II and is feminine.<fnref n="5" /> As expected, semantic core rules do indeed take precedence, so a noun like *djadja* ‘uncle’, which belongs to inflection class II and would on that basis be feminine, is in fact masculine by virtue of its semantics. For the data, see Corbett 1991:34–43, and for a detailed analysis, see Fraser & Corbett 1995.

While prediction according to inflection class applies in principle to all nouns (there are generalizations even for those that do not inflect), languages may also have principles restricted to complex nouns. A well-known example is German’s last-member principle (*Letzt-Glied-Prinzip*), for which see Köpcke & Zubin 1984:28–29 and references there. According to this principle, the gender of the whole word is determined by the gender of the last element. Another example of a structural morphological assignment principle can be found in V+N compounds in various Romance languages, such as French *tire-bouchon* (M) (pull-cork) ‘corkscrew’. Almost all of these are masculine by virtue of their structural type as V+N compounds, rather than taking the gender cue from the noun involved in the compound: for example, French *ouvre-boite* (M) (open-tin) ‘tin opener’ or Italian *stendibiancheria* (M) (*stendi-biancheria*; hang.up-laundry) ‘laundry rack’, where both *la boite* (F) ‘tin’ in French and *la biancheria* (F) ‘laundry’ in Italian are feminine.

Phonological principles also vary in generality. In the Cushitic language Qafar (Parker & Hayward 1985, Corbett 1991:51–52), as expected, semantic principles apply (nouns denoting males are masculine, and those denoting females are feminine). For the semantic residue, the gender of a noun can be predicted from the phonological shape of the stem. Nouns whose citation form ends in an accented vowel are feminine, such as *karmà* ‘autumn’; the rest are masculine, such as *gilàl* ‘winter’ (with final consonant), *tàmu* ‘taste’ (final vowel, but not accented). Since semantic rules take precedence and phonological rules apply only to the residue, a noun like *abbà* ‘father’, whose accented vowel would otherwise predict feminine, is in fact masculine following its semantics. Languages may also employ phonological principles relating to syllable count and phonotactic patterning. As implied earlier, systems with semantic assignment supplemented by morphological and/or phonological assignment rules account for around half of gender systems (59/112, or 53%).

Below we discuss these in more detail for German. At this point it suffices to say that almost two thirds of monosyllabic German nouns are masculine, as are three quarters of German nouns with the phonotactic description CC_CC, for example, *Flachs* [flaks] ‘flax’ (Köpcke 1982, Köpcke & Zubin 1983, 1984).

<footnote n="5">⁵ The prediction goes in this direction, for two reasons: (i) predicting three genders from four inflection classes yields good results, while the reverse is much less successful; (ii) in use, any noun must be in some morphological form, but it may not require gender agreement. For the directionality argument, see especially Corbett & Fraser 2000, and Nesset 2003 on Ukrainian.</footnote>
4. **GERMAN GENDER: ESSENTIALS.** Here we present what is needed to appreciate the issues involved. There are two key parts of a gender system: how gender is realized and how gender is assigned. The evidence for gender is provided by the agreement system (Hockett 1958, Corbett 1991). This component of the German gender system is relatively straightforward. The examples in 1 above show that within the nominal phrase there is clear evidence for a gender system, and one with three values. There is no reflection of gender in the predicate in Standard German, but the relative and third-person pronouns both distinguish gender. Most nouns induce the same gender value within the nominal phrase (on the article and other modifiers) and on the relative and personal pronouns. Such nouns have a 'consistent agreement pattern' (Corbett 2012:85-88) and are the focus of our enquiry. The minority, the 'hybrid' nouns that show different values for different agreement targets, are excluded here, as noted earlier, though they are of great interest for other reasons (see n. 3 above).

Assignment is a major issue for German. How is it that native speakers 'know' that *Film* 'film' is masculine in German, that *Symphonie* 'symphony' is feminine, and that *Buch* 'book' is neuter? They have the evidence from realization (they are frequently exposed to the agreement targets in the input), but they need to know the gender value in order to produce the appropriate forms. The model of this knowledge is termed a 'gender assignment system', and this is our topic.<fnref n="6" /> We follow the typology above in setting out the main generalizations.

4.1. **SEMANTIC ASSIGNMENT.** Like all gender systems the German system shows semantic regularities. Sex-differentiable nouns, that is, nouns that refer to male or female humans or male or female (higher) animals, are assigned their gender on the basis of biological sex: *Frau* (F) 'woman' and *Kuh* (F) 'cow' are feminine; *Mann* (M) 'man' and *Bulle* (M) 'bull' are masculine. Where the sex is not apparent, or not of interest to humans, animals often receive conventionalized gender, where one noun is used for referents of either sex, for example, *Affe* (M) 'ape, monkey', *Ameise* (F) 'ant', and *Krokodil* (N) 'crocodile'.<fnref n="7" />

For most German nouns, however, including the inanimate nouns in 1, gender assignment is based on formal principles: that is, it follows their morphology (compounding, derivation, inflection class) or their phonology (stem shape). We discuss these in turn.

4.2. **MORPHOLOGICAL ASSIGNMENT.** There are two sides to morphological assignment: word formation and inflection. We start with word formation, where overall prediction rates are lower than in inflection.

<footnote n="6">⁶ According to Thornton (2009:14), linguists use the metaphor of containers when talking about genders (assigning nouns to genders), and from the opposite perspective, they speak of assigning genders to nouns so that they function correctly in the syntax. Both perspectives are valid. Here we take the latter perspective—given the lexical entry of a noun, we ask how its gender value is assigned.</footnote>

<footnote n="7">⁷ Anaphoric pronouns agree with the antecedent in gender, so if the antecedent is the neuter noun *Krokodil* the anaphoric pronoun has to be *es* 'it'. If one knows and wants to express the sex of a crocodile, this needs to be clearly marked morphologically on the noun. For higher animals, derivation with *-in* is usually possible, such as *Affin* 'female ape, monkey'. Only then is the feminine pronoun *sie* possible. Typically, and for lower animals exclusively, a compound with *-männchen* (for males) or *-weibchen* (for females) is used, for example, *Ameisenmännchen* 'male ant' and *Krokodilweibchen* 'female crocodile', where *er* or *sie*, respectively, are possible in semantic agreement with the antecedent. The neuter pronoun *es* remains a possibility as well, since compounds in *-männchen* or *-weibchen* are hybrids that require agreement in neuter gender in the noun phrase and the relative pronoun, but allow semantic agreement in the anaphoric and possessive pronouns.</footnote>
DERIVATION. Morphologically complex German lexemes are typically governed by the LAST-MEMBER PRINCIPLE (*Letzt-Glied-Prinzip*): the gender of the whole word is determined by the gender of the last element (Köpcke & Zubin 1984:28–29, and references there). According to this general principle, important properties of a German noun lexeme are typically determined by the last member. We can illustrate this from part-of-speech specification. Thus *Gutmensch* (M) ‘do-gooder’ is a compound consisting of the adjective *gut* ‘good’ and the noun *Mensch* (M) ‘human being’. By the last-member principle, the compound is a noun. The last-member principle is a major factor in gender assignment, and it is particularly interesting regarding what exactly counts as the ‘last member’:

(i) if the final item of a compound is a simplex noun, this determines the gender of the compound. For example, *Mutterschutz* ‘maternity’ consists of the feminine first member *Mutter* ‘mother’ and the masculine last member *Schutz* ‘protection’; by the last-member principle it is masculine.

(ii) if the final item of a compound is itself complex, it is the last noun (including any derivational prefixes or suffixes) that counts (Corbett 1991:50). So *Königspetschaft* ‘royal seal’ is neuter because *Petschaft* ‘seal’ is neuter, though *-schaft* derives feminine nouns. This also holds in nouns derived with the prefix *ge-*. For example, *Kirchengestühl* ‘pews (collective)’ is neuter. Starting from the masculine noun *Stuhl* ‘chair’, the derived noun *Gestühl* ‘seating’ is neuter; this is the last member, so adding the feminine noun *Kirche* ‘church’ (the *-n* being a linking element) has no effect on the gender of the compound.

Derivational suffixes can also determine gender. For example, the suffixes *-heit*, *-keit*, and *-schaft* derive nouns of feminine gender. In the case of a word like *Heiserkeit* (F) ‘hoarseness’, there is no doubt about the outcome, since it is derived from an adjective *heiser* ‘hoarse’. The suffix *-keit*, being the last member, is sufficient to determine that the derived noun is feminine and has no competition. More interesting are nouns like *Freundschaft* (F) ‘friendship’; here the base is the masculine noun *Freund* ‘friend’, but the derived noun is feminine. Similar is *Landschaft* (F) ‘landscape’, derived from the neuter noun *Land* ‘land’. Here the effect of the last member (the suffix *-schaft*) is clear; gender is assigned to the derived word as a whole, and this is irrespective of the gender of the base, if a noun.

The last-member principle works well with productive suffixes. Almost all nouns derived with the productive suffixes *-heit*, *-keit*, and *-schaft* are feminine, the only exception being the neuter *Petschaft* ‘seal, stamp’ (Augst 1975:27), mentioned above.<fnref n="8" /> Less productive derivational affixes, however, are often a less good predictor of gender; thus *-el* derives nouns with different gender values, such as *Würfel* (M) ‘die, cube’, *Klingel* (F) ‘bell’, and *Kürzel* (N) ‘abbreviation’. Of the forty-two nouns in our database derived with *-el*, 50% are masculine, 17% feminine, and 33% neuter.

Given the last-member principle, we would not expect prefixes to have any effect, and this is basically what we find, as shown by the following triplet: each of *Sinn* (M) ‘sense’, *Zeit* (F) ‘time’, and *Wort* ‘word’ (N) can be prefixed with *un-*, giving *Unsinn* (M) ‘nonsense’, *Unzeit* (F) ‘untimeliness’, and *Unwort* (N) ‘offensive word’. The prefix *un-* has no effect: the gender of the base is retained. There are exceptions to this, however:

<footnote n="8">⁸ This is not originally a word derived with *-schaft* but a borrowing from Czech (*pečet*'), which has been remodeled on the basis of derived nouns with this suffix (https://www.dwds.de/wb/Petschaft).</footnote>
the prefix *ge-* is associated with neuter gender, for example, *Gesuch* (N) ‘petition’ from the verb *suchen* ‘seek’ and *Gebüsch* (N) ‘shrubbery’ from *Busch* (M) ‘bush’.

We note two further derivational rules that predict the gender value without exception:

(i) infinitives can be regularly converted to nouns of neuter gender, for example, *Lachen* ‘laughing’ from the infinitive *lachen* ‘laugh’, and

(ii) verbal bases can be converted to nouns of masculine gender, for example, *Einkauf* ‘purchase’ from *einkaufen* ‘buy’ (Schäfer 2018:233). This illustrates again that parts of the derivational system are fully regular in terms of gender assignment.

INFLECTION. German nouns are typically analyzed as belonging to more than a dozen inflection classes (see, for example, Pavlov 1995:44, Cahill & Gazdar 1999). The numbers of members show considerable differences, with some clear majority patterns (see below). The typical paradigm has eight cells, with two number values, four case values, and extensive syncretism. Three example paradigms are given in 3–5; principal parts (see below) are highlighted in gray.

(3) Forms for German *Lampe* ‘lamp’ (genitive SG -Ø, nominative PL -(e)n)<fnref n="9" />

<table><thead><tr><th></th><th>SINGULAR</th><th>PLURAL</th></tr></thead><tbody><tr><th>NOMINATIVE</th><td>Lampe</td><td>Lampen</td></tr><tr><th>ACCUSATIVE</th><td>Lampe</td><td>Lampen</td></tr><tr><th>GENITIVE</th><td>Lampe</td><td>Lampen</td></tr><tr><th>DATIVE</th><td>Lampe</td><td>Lampen</td></tr></tbody></table>

While *Lampe* (F) ‘lamp’ has syncretism of all case forms, distinguishing only singular from plural, other nouns make more distinctions; thus *Tag* (M) ‘day’ in 4 shows evidence for the genitive and dative.

(4) Forms for German *Tag* ‘day’ (genitive SG -(e)s, nominative PL -e)

<table><thead><tr><th></th><th>SINGULAR</th><th>PLURAL</th></tr></thead><tbody><tr><th>NOMINATIVE</th><td>Tag</td><td>Tage</td></tr><tr><th>ACCUSATIVE</th><td>Tag</td><td>Tage</td></tr><tr><th>GENITIVE</th><td>Tag(e)s</td><td>Tage</td></tr><tr><th>DATIVE</th><td>Tag</td><td>Tagen</td></tr></tbody></table>

Finally, *Zeuge* (M) ‘witness’ shows evidence for the accusative, distinct from the nominative.

(5) Forms for German *Zeuge* ‘witness’ (genitive SG -(e)n, nominative PL-(e)n)

<table><thead><tr><th></th><th>SINGULAR</th><th>PLURAL</th></tr></thead><tbody><tr><th>NOMINATIVE</th><td>Zeuge</td><td>Zeugen</td></tr><tr><th>ACCUSATIVE</th><td>Zeugen</td><td>Zeugen</td></tr><tr><th>GENITIVE</th><td>Zeugen</td><td>Zeugen</td></tr><tr><th>DATIVE</th><td>Zeugen</td><td>Zeugen</td></tr></tbody></table>

Despite the syncretism, the principal parts of the paradigm (Stump 2016:257–59) allow us to define each inflection class unambiguously; as seen in 3–5, these are the genitive singular and nominative plural forms. For example, the feminine noun *Lampe* ‘lamp’ belongs to the inflection class -Ø/-(e)n (genitive singular *Lampe* and nominative plural *Lampen*), and the masculine noun *Tag* ‘day’ belongs to the inflection class -(e)s/-e (genitive singular *Tag(e)s* and nominative plural *Tage*).

<footnote n="9">⁹ What is orthographically represented as <e> in nominal suffixes is pronounced as schwa [ɔ].</footnote>
These three nouns give a fair impression of the inflectional system of German nouns, in that nouns typically distinguish the case values to a limited extent. This hampers acquisition, since in context a noun's form may not reliably indicate its inflection class (which may be a predictor of its gender value). Conversely, the articles are frequent, and the definite article distinguishes gender, number, and case moderately well, as seen in 6.

(6) Forms of the German definite article

<table><thead><tr><th rowspan="2"></th><th colspan="3">SINGULAR</th><th>PLURAL</th></tr><tr><th>MASCULINE</th><th>FEMININE</th><th>NEUTER</th><th></th></tr></thead><tbody><tr><th>NOMINATIVE</th><td>der</td><td>die</td><td>das</td><td>die</td></tr><tr><th>ACCUSATIVE</th><td>den</td><td>die</td><td>das</td><td>die</td></tr><tr><th>GENITIVE</th><td>des</td><td>der</td><td>des</td><td>der</td></tr><tr><th>DATIVE</th><td>dem</td><td>der</td><td>dem</td><td>den</td></tr></tbody></table>

Various syncretisms in the definite article mean that in some combinations the gender is clear and in some it is not (see the dative, for instance). All gender contrasts are neutralized in the plural. Since the articles are frequent, the gender system is an integral part of German syntax, providing ample evidence of gender for the child learner;<fnref n="10" /> see Kupisch, Geiss, et al. 2022:4–7 for a recent survey on acquisition, and Audring 2019 for discussion.

In some instances, gender can be predicted unambiguously (or nearly unambiguously) from the inflection class. So, for example, all nouns of inflection class -Ø/-(*e*)n— the class that *Lampe* ‘lamp’ belongs to—are feminine, and the same is true of all nouns that inflect with -Ø in the genitive singular and have a stem change plus suffix -e in the nominative plural. But all nouns following inflection class -(*e*)n/-(*e*)n are masculine. Then there are several inflection classes whose nouns cannot be feminine: for instance, no inflection class with -s in the plural contains any feminine nouns, nor does any inflection class with -(*e*)s in the genitive singular. For example, we can predict that *Knopf* ‘button’ (a masculine noun following inflection class -(*e*)s/stem change + -e) cannot be feminine based on its paradigm (7).

(7) Forms for German *Knopf* ‘button’ (genitive SG -(*e*)s, nominative PL stem change + -e)

<table><thead><tr><th></th><th>SINGULAR</th><th>PLURAL</th></tr></thead><tbody><tr><th>NOMINATIVE</th><td>Knopf</td><td>Knöpfe</td></tr><tr><th>ACCUSATIVE</th><td>Knopf</td><td>Knöpfe</td></tr><tr><th>GENITIVE</th><td>Knopf(*e*)s</td><td>Knöpfe</td></tr><tr><th>DATIVE</th><td>Knopf</td><td>Knöpfen</td></tr></tbody></table>

To tell masculines apart from neuters is less straightforward for these inflection classes, but further phonological cues often help. We know that *Knopf* is very likely masculine, given that almost all monosyllabic German nouns starting with the consonant cluster /kn/ are masculine, the only exception being the neuter noun *Knie* ‘knee’ (Köpcke & Zubin 1984:29–30).

<footnote n="10">¹⁰ German nouns are often quoted with the article as a sign of their gender. This is fine as a convenience, but can be misleading, since the definite article is only an indicator, albeit a powerful one. When we say that a noun is of, say, neuter gender, this means that it takes neuter agreement consistently, for all agreement targets rather than just for the article. For the role of the article, see Dye et al. 2017. Note too that there is a trade-off between articles and adjectives; in fuller phrases, if the article is uninformative, the adjectival inflection can provide the relevant contrast, for example, *ein neuer Film* (M) ‘a new film’ vs. *ein neues Buch* (N) ‘a new book’, where the indefinite article *ein* does not differentiate gender, but the adjective *neuer* vs. *neues* does.</footnote>
Since German nouns inflect in numerous different ways, the logical approach is to take these patterns as predictors for the three gender values. This fits with the typological picture, where inflection regularly appears as a strong predictor (Corbett 1991:34–50).<fnref n="11" /> Naturally we wish to validate the claim for German, and to show more clearly than before the place of inflection in the overall scheme of German gender assignment. In this section we give the big picture, and fuller detail can be found in the online supplementary materials.<fnref n="12" /> We outline two broad approaches to patterns of inflection, which we characterize as top-down vs. bottom-up, and show how each sheds light on the complex German system.

The top-down approach starts from the observation that inflectional properties (stem alternations as well as inflections) are not scattered randomly across the noun inventory. Rather, there are patterns of implication: as just one example, nouns that have the accussative plural in -(e)n always have this ending in the genitive plural as well. Given these patterns, we divide nouns into inflection classes according to their complete inflectional behavior. As a baseline from which we can calibrate, (i) canonical inflection classes are fully comparable and are distinguished as clearly as is possible, and (ii) the distribution of lexical items over canonical inflection classes is synchronically unmotivated (Corbett 2009). We might expect this to be a purely idealized measure, but it is realized, if rarely. German represents a more common, less canonical situation, with some inflection properties shared across inflection classes, and hence different levels of generalization. Establishing the optimal analysis is not straightforward: while agreement evidence allows us to show that there are indeed three gender values in German, there is no similar test to show that we have the right number of inflection classes. As ever, we rely on criteria such as coverage of the data and simplicity. The best account to date, in our view, is that of Cahill and Gazdar (1999), who work with thirteen inflection classes, structured in a default inheritance hierarchy. They make the important point that phonology is insufficient to predict inflectional behavior in German (1999:10); hence the need for inflection class information. Impressive though it is, we cannot simply plug in their analysis, since they use gender as a possible predictor for inflection class (1999:24), while we are demonstrating the predictability of gender.

The bottom-up approach starts from the raw data of the inflectional behavior of each noun. The different patterns, with their varying degrees of generality, are to be discovered rather than being given. We cannot assume that partitioning will be adequate; rather, we may need semilattices, where one subclass is allowed to belong to more than one superclass (Beniamine 2021). Prima facie, this is a better fit with our general approach. Hence when we invoke inflectional properties, we use the indicators from the CELEX corpus (Baayen et al. 1995) (with obvious errors corrected; see supplementary materials) as no more than an abbreviation for the full set of inflections. That is, nouns with the same indicator have strictly identical behavior (of stem alternation as well as of inflection); we do not include a level of analysis allowing for minor deviations, subclasses, and so on (see further in §5.5).

Given the effort we have put into cleaning and analyzing the CELEX database, it is worth asking what it tells us about the different approaches to inflection classes. Of the 30,576 nouns included, a partitioning into just six inflection classes accounts for the

<footnote n="11">¹¹ In the past, the perspective on German gender and inflection class tended rather to use gender to predict inflection (see e.g. Augst 1975:24–36, Bittner 1999). More recently, one also finds approaches where both gender and inflection class have predictive value (Kürschner & Nübling 2011).</footnote>

<footnote n="12">¹² All supplementary materials can be accessed at http://muse.jhu.edu/resolve/294.</footnote>
behavior of 26,000 of them. At the other extreme, there are eighty nouns with unique behavior, and almost 150 types in all that have ten or fewer members. These smaller groupings include numerous borrowings.<fnref n="13" /> Thus, the analysis into inflection classes represents an important property of the German system: these are patterns that account for the majority of nouns. Conversely, the diachronic reduction of the paradigms, both in the shrinkage of the case system and in the loss of affixal distinctions within the remaining system, has produced a situation in which a substantial minority of nouns are heteroclites (i.e. take forms from different major patterns) or are overabundant (i.e. have alternative possibilities; Thornton 2019). For these, rather than simple partitioning, the richer lattice approach makes sense.

4.3. **PHONOLOGICAL ASSIGNMENT.** The phonological shape of the noun stem often allows prediction of gender. Phonological shape does not provide exceptionless assignment, but statistical tendencies. For example, most trochaic nouns ending in schwa are feminine, such as *Lampe* ‘lamp’ and *Sprache* ‘language’, but there are also (some) masculine nouns, such as *Zeuge* ‘witness’, and (a very few) neuter nouns like *Ende* ‘end’ that satisfy the same phonological description. Köpcke (1982) and Köpcke and Zubin (1984) establish a number of phonological rules to account for the gender of monosyllabic nouns: for example, most monosyllabic nouns are masculine, and as noted above, almost all monosyllabic nouns that begin with the cluster /kn/ are masculine. This holds for our corpus as well: 64% of monosyllabic nouns are masculine, and 93% of monosyllabic nouns with initial /kn/ are masculine, the only exception being the neuter noun *Knie* ‘knee’. So for the noun *Knopf* ‘button’, its initial cluster points overwhelmingly to masculine, which indeed it is. Table 2 gives gender distributions for selected phonological rules in Köpcke 1982 and Köpcke & Zubin 1984:29. (Absolute numbers are in parentheses; the column ‘other rules’ refers to the percentage of nouns correctly covered by semantic and morphological assignment rules, which in Köpcke’s (1982) model apply before all phonological rules.)

<table><caption>TABLE 2. Selected phonological rules (Köpcke 1982, Köpcke & Zubin 1984:29).</caption><thead><tr><th></th><th>M</th><th>F</th><th>N</th><th>OTHER RULES</th><th>TOTAL</th><th>EXAMPLE</th></tr></thead><tbody><tr><td colspan="7"><strong>RULES ASSIGNING MASCULINE</strong></td></tr><tr><td>/kn_</td><td>93% (14)</td><td>0% (0)</td><td>7% (1)</td><td>0% (0)</td><td>100% (15)</td><td>Knopf 'button'</td></tr><tr><td>/dr_, /tr_</td><td>89% (47)</td><td>0% (0)</td><td>2% (1)</td><td>9% (5)</td><td>100% (53)</td><td>Dreck 'dirt'</td></tr><tr><td>/fC_</td><td>86% (144)</td><td>1% (2)</td><td>5% (8)</td><td>8% (13)</td><td>100% (167)</td><td>Stern 'star'</td></tr><tr><td>/CC_CC_</td><td>70% (75)</td><td>4% (4)</td><td>10% (11)</td><td>16% (17)</td><td>100% (107)</td><td>Frost 'frost'</td></tr><tr><td>/_NC_</td><td>75% (111)</td><td>5% (7)</td><td>5% (8)</td><td>15% (22)</td><td>100% (148)</td><td>Dank 'gratitude'</td></tr><tr><td colspan="7"><strong>RULES ASSIGNING FEMININE</strong></td></tr><tr><td>/_(C)ft/, /_(C)xt/, /_(C)çt/</td><td>7% (4)</td><td>64% (35)</td><td>0% (0)</td><td>29% (16)</td><td>100% (55)</td><td>Zunft 'craft'</td></tr><tr><td>/_ur/ or /_yr/</td><td>6% (1)</td><td>71% (12)</td><td>0% (0)</td><td>23% (4)</td><td>100% (17)</td><td>Tür 'door'</td></tr><tr><td colspan="7"><strong>RULES ASSIGNING NEUTER</strong></td></tr><tr><td>/_et/</td><td>14% (1)</td><td>0% (0)</td><td>86% (6)</td><td>0% (0)</td><td>100% (7)</td><td>Duett 'duet'</td></tr></tbody></table>

As far as phonological rules are concerned, there are problems of applicability because the rules are not properly defined for the full set of German nouns. Since Köpckian rules are defined for monosyllabic nouns, it is not entirely clear how they apply to polysyllabic nouns. Köpcke (1982:1–3) points out that his study of gender assignment

<footnote n="13">¹³ There are also many loanwords in the larger inflection classes. Thus being a loanword is not in itself a predictor of gender, so it is logical to work from the formal property of inflection.</footnote>
in monosyllabic nouns was prompted by the fact that this was typically considered arbitrary, whereas the gender of polysyllabic words could be accounted for by appealing to the last-member principle, the prefix *ge*-, or ending in schwa. Since polysyllabic nouns are not covered by his rules, we need to extend phonological principles to them. Further, it is sometimes not fully clear how a rule applies to monosyllables either, for example, the rule *CC_CC*. It is not specified whether this rule only applies to CCVCC structures or also includes *CCC_CC* and *CC_CCC* structures, nor how many and which segments the underscore is allowed to match.

The second issue with Köpckian rules is that they are not exhaustive. By this we mean that Köpcke made claims about words that match these patterns, but no claims about words that do not. Consequently, we are left with many nouns for which we have no explicit pattern to test. If we confine ourselves to monosyllables, and depending on the explicit implementation of the rules (see the points about applicability above), we achieve a coverage of slightly more than 400 of 1,365 monosyllabic nouns in our database, amounting to approximately one third. But what can we say about the rest?

**4.4. OVERLAP OF PREDICTORS.** Figure 1 gives a taste of how these different semantic, morphological, and phonological principles or predictors overlap and form intersecting sets. We are looking at a part of the system, namely at the semantic predictor ‘is female’, the morphological predictor ‘inflects like *Lampe*’, and the phonological predictor ‘ends in schwa’, each of which predicts feminine gender.

<figure><img src="image_6.png" /><figcaption>FIGURE 1. Overlap of semantic, morphological, and phonological predictors; IC = inflection class.</figcaption></figure>

The nouns *Dame* ‘lady’, *Mutter* ‘mother’, *Frau* ‘woman’, and *Heilige* ‘female saint’ are feminine due to their semantics; they refer to females. The nouns *Dame* ‘lady’, *Frau* ‘woman’, *Uhr* ‘clock’, and *Lampe* ‘lamp’ are feminine due to the way they inflect, namely having the genitive singular in zero and the nominative plural in -(e)n. The intersecting set contains *Dame* and *Frau*, for which these two predictors point in the same direction. Finally, we have the nouns *Dame*, *Lampe*, *Heilige*, *Illustrierte* ‘magazine’,<fnref n="14" /> *Zeuge* ‘witness’, and *Ende* ‘end’ in the set ‘ends in schwa’, again intersecting

<footnote n="14">¹⁴ The noun *Illustrierte* (F) ‘magazine’ inflects like an adjective (-n/-n), so it is different from *Lampe*. This applies to *Heilige* (F) ‘female saint’ as well, which unlike *Illustrierte* refers to a woman.</footnote>
with the previous sets. However, there are nonfeminines in this set: *Zeuge* 'witness' is taken care of by a different semantic principle; it refers to a male and is therefore masculine (arrow on the right), and *Ende* 'end' is at least not feminine by the way it inflects (arrow on the left). We say more about *Ende* toward the end.

It is worth stepping back for a moment to consider the implications of overlapping predictors for individual nouns. Early work on gender assignment took what we may call the parsimonious perspective. Given possible overlapping predictors, the aim was to establish which one was 'really' the predictor. This perspective is in line with the default scientific approach, perhaps reinforced by a computing perspective (at a time when storage was expensive and so processing was preferred over storing). In terms of method, researchers set up the system in the most economical fashion: once a noun is predicted correctly by a predictor it is put to the side as 'accounted for', and we do not evaluate further predictors with respect to this noun. Increasingly researchers now also take a 'reinforcing perspective'. Multiple predictors can point in the same direction, reinforcing each other and thus facilitating learning and retention. Given what we now know of the brain's storage potential, the reinforcing perspective gains validity. Studies in corpus and cognitive linguistics have drawn attention to the fact that multiple factors can overlap and play a role in explaining a phenomenon (Malkiel 1978, Bybee & Slobin 1982, Bresnan et al. 2007, Bresnan & Hay 2008, Levshina 2016, Copot et al. 2022, among many others). One of the insights from this line of research is that often no single cue predicts some linguistic outcome, but rather multiple, partially overlapping cues can be present at the same time.

5. NEW DATA AND METHODS. In analyzing the German gender system there are at least three pitfalls. The first is ‘cherry picking’: observations of alleged regularity are sometimes based on few examples, and the overall applicability of these regularities is left unexplored. Some alleged rules might look convincing on the positive evidence that is presented, but they do not stand up to scrutiny. For instance, it has been claimed that ‘functional hollows’ are neuter in German (see Rice 2006, Steinmetz 2006), that is, nouns that refer ‘to a disk or to a complete or partial enclosure, whereby the hollow portions thereof are functional in that they are criterial for defining the object in question’ (Steinmetz 2006:1434), for example, *Rad* (N) ‘wheel’. The suggestion is not psychologically plausible, and it is hard to test and fraught with counterexamples (Enger 2009:1291).

Second, often we find generalizations without a baseline: thus, a prediction of a particular gender value for, say, 45% of the nouns is hardly remarkable if 45% of the nouns overall are of that gender. It is important to establish such a baseline clearly; this is not straightforward, because the baseline varies depending on the data set, as we discuss later.

Third, we may miss the importance of overlapping factors: semantic, morphological, and phonological properties may make the same gender value more probable, so we have to be careful about making a claim for a particular generalization AND we need to investigate overlap as a part of the system.

5.1. DATA. Taking a more holistic view, we mine a database of 30,576 German nouns from the CELEX database (Baayen et al. 1995),<fnref n="15" /> based on the Mannheim corpus (~6 million words), annotated for gender, phonological shape, inflection class, and

<footnote n="15">¹⁵ For availability, see http://celex.mpi.nl.</footnote>
derived/compounded status. To this we added semantic information (human, animal, object, abstract, mass) and also frequency, based on the much larger DECOW16b corpus (Schäfer & Bildhauer 2012, Schäfer 2015). We also fixed inconsistencies in the original annotation in CELEX (see supplementary materials).

The present study has a massive advantage over previous work. At last, we have a large and clean data set, set up to allow interrogation with cutting-edge computational methods. But we need to bear in mind that even in a substantial corpus there are interesting phenomena that are hardly represented. We discuss two significant instances here. The first concerns human proper names, such as *Anna* or *Holger*. For human proper names, assignment is 100%, apart from unisex names, such as *Kim*, and the gender of a proper name can be deduced from the sex of the referent. If someone invents a new name for their daughter, we know it is feminine. This means that the semantic principles are actually better grounded than our statistics allow for.

The second instance is fringe nonce assignment. In order to explain what we mean by this we need to take a step back. Many gender languages have semantic clusters that are associated with a certain gender. For example, cocktails in German are masculine, such as *Caipirinha*, presumably because the basic-level hypernym *Cocktail* is masculine.<fnref n="16" /> Such noncore assignment principles have been studied in detail: see, for example, Köpcke & Zubin 1983, 1984, 1996, Zubin & Köpcke 1984, Fahlbusch & Nübling 2014, and Fedden, Audring, & Corbett 2026. They have also been established for other languages, such as Italian, among others (Thornton 2009), and Norwegian (Enger 2009). Fringe nonce assignment is linked to basic-level categories, which is the preferred level of naming and categorization of objects (Rosch et al. 1976; for an overview, see Hajibayova 2013). It is at the basic level that we find optimal categorization in terms of balance between informativeness and mental burden due to the number of distinctions (Tversky 1986:65).<fnref n="17" />

These two sets of nouns, human proper names and fringe nonce assignments, have important similarities. Individually they are infrequent in a balanced corpus, yet speakers are 100% certain of their gender assignment. Thus, the fringe is more transparent than the core.

The overall distribution in our corpus of 36% masculine, 45% feminine, and 19% neuter (given in Table 1 above) might tempt us to think about German as having 36% masculine, 45% feminine, and 19% neuter nouns. We have to be careful, however, not to take these proportions for granted. It is not even clear which gender value has the most nouns; all depends on the data set. Since proportions are always relative to the sample,

<footnote n="16">¹⁶ Such rules are called ‘crazy’ rules by Enger (2009), ‘crazy’ in the sense that they are semantic gender assignment principles that are separate from the semantic core rules in a language (that is, males are masculine and females are feminine for German and many other languages). This usage is by analogy with crazy rules in phonology, which are phonological rules without any phonetic grounding (Bach & Harms 1972). For experimental investigation of noncore semantic assignment, see Schwichtenberg & Schiller 2004.</footnote>

<footnote n="17">¹⁷ There are fascinating cases where the gender of a semantic cluster is not derived from a basic-level term. For example, names of ships are feminine in German, such as *die Titanic*. It is clearly not the case that speakers are using an ‘invisible’ basic-level term denoting the type of object the name is referring to in order to establish the gender, since *das (Schlacht-)Schiff* ‘(battle)ship’ is neuter in German, rather than feminine (see Köpcke & Zubin 2005:118–19). And it is getting even more complicated. Ships can of course be named after men, which gives a situation where the rule for a man’s name conflicts with the ship rule predicting feminine. Here the ship rule holds; for example, the battleship *die Bismarck* was named after Otto von Bismarck, and only feminine is possible; similarly, *die Prinz Eugen* is feminine.</footnote>
they can vary widely, depending on which nouns we take to calculate them. This is illustrated in Table 3 (adapted and expanded from Opitz & Pechmann 2016:237).<fnref n="18" />

<table><caption>TABLE 3. Proportions of German gender values in different data sets.</caption><thead><tr><th rowspan="2">SOURCE</th><th colspan="4">GENDER VALUES</th></tr><tr><th colspan="2">MASCULINE</th><th colspan="2">FEMININE</th><th colspan="2">NEUTER</th></tr><tr><th></th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th></tr></thead><tbody><tr><td>Augst 1975</td><td>66%</td><td>1,425</td><td>13%</td><td>289</td><td>21%</td><td>449</td></tr><tr><td>Köpcke 1982</td><td>43%</td><td>1,758</td><td>38%</td><td>1,567</td><td>19%</td><td>788</td></tr><tr><td>Krifka 2009</td><td>45%</td><td>268</td><td>33%</td><td>194</td><td>22%</td><td>133</td></tr><tr><td>Pavlov 1995</td><td>41%</td><td>2,633</td><td>42%</td><td>2,726</td><td>18%</td><td>1,146</td></tr><tr><td>CELEX (types)</td><td>35%</td><td>10,788</td><td>45%</td><td>13,688</td><td>20%</td><td>6,045</td></tr><tr><td>CELEX (tokens)</td><td>35%</td><td>380,770</td><td>42%</td><td>447,048</td><td>23%</td><td>248,147</td></tr></tbody></table>

But even in one and the same sample we see different distributions at different frequency bands. Gender proportions in our data set are significantly different depending on the frequency of nouns, for which we use the DECOW16b corpus (Table 4). Figure 2 gives the same data but shows more clearly that we find change in gender proportion only for high-frequency ranks, after which the proportions settle quickly and remain stable.

<table><caption>TABLE 4. Overall gender distribution in German (DECOW16b corpus; rounded to full percentages).</caption><thead><tr><th>MOST FREQUENT ...</th><th>MASCULINE</th><th>FEMININE</th><th>NEUTER</th></tr></thead><tbody><tr><td>100 nouns</td><td>29%</td><td>31%</td><td>40%</td></tr><tr><td>1,000 nouns</td><td>36%</td><td>41%</td><td>23%</td></tr><tr><td>5,000 nouns</td><td>36%</td><td>44%</td><td>20%</td></tr><tr><td>10,000 nouns</td><td>36%</td><td>45%</td><td>19%</td></tr><tr><td>20,002 nouns</td><td>36%</td><td>45%</td><td>19%</td></tr><tr><td>30,919 nouns</td><td>36%</td><td>45%</td><td>19%</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>FIGURE 2. Continuous plot of overall gender distribution in German (DECOW16b corpus).</figcaption></figure>

<footnote n="18">¹⁸ The slightly different numbers for CELEX (types) in this table compared to ours are likely due to the fact that we excluded hybrids and also manually corrected some errors in CELEX, which subtly changed gender proportions with respect to the unmodified CELEX. For additional, mainly smaller, corpus counts, see Binanzer 2017:51 and Corteen 2018:41.</footnote>
The difference in proportions depending on frequency-based sample size is revealing. A key factor is that a larger sample contains more derived nouns, and derivation favors the feminine in German. So there is not one right answer about gender proportions; there is more structure to the lexicon than that. This distribution raises intriguing questions for psycholinguistics, especially for language acquisition, which starts with the least regular part of the lexicon for gender assignment; that is, the child learner is first exposed to the hardest part of the task (cf. Mills 1986:49–50). We do not take this point further here, since the DECOW16b corpus is a general one and not a corpus of child-directed speech. Another point to take into account is the ongoing stream of borrowings entering the language, which may affect the overall balance (as has been the case in French) without having an effect on the frequent core vocabulary.

Thus, the frequency-based distributions demonstrate the need for caution when citing gender distributions for other languages, since they vary according to the frequency range of the nouns included in the sample. In the remainder of the article, we work with the whole sample of nouns with a consistent gender value (hence excluding hybrids).

5.2. ANALOGICAL CLASSIFICATION. Our methods for semantic and phonological representations follow the hypothesis that words which are semantically or phonologically similar will belong to the same class. While earlier work on analogical classification was based on hand-crafted schemata (Bybee & Slobin 1982), more recent studies rely on computational methods, which can automatically induce the patterns in the data. Among these methods we find ANALOGICAL MODELING (AM; Skousen 1989, Arndt-Lappe 2011, 2014), TILBURG MEMORY-BASED LEARNER (TiMBL; Daelemans et al. 1998), MINIMAL GENERALIZATION LEARNER (Albright & Hayes 2002), and NEURAL NETWORKS (Matthews 2005, Guzmán Naranjo 2019, 2020). While we cannot give a full discussion of these techniques here, the principle is the same: class assignment proceeds on the basis of phonological and semantic properties of the items (see Guzmán Naranjo 2019 and Matthews 2005 for a detailed discussion and comparison of the different techniques).

Most previous approaches to analogical classification represent the phonology of the lexical items as the *N* final segments of the base.<fnref n="19" /> Each segment position is a categorical predictor, and each possible value gets a coefficient or weight. The addition of these weights predicts the class of the lexeme in question. For example, to represent the nouns *Blick* ‘gaze’, *Scheik* ‘sheik’, *Schick* ‘chic’, *Teig* ‘dough’, and *Tick* ‘tic’ this way, we can build a table (Table 5).

<table><caption>TABLE 5. Phonological representation of example nouns as phoneme strings.</caption><thead><tr><th>LEXEME</th><th>GENDER</th><th>PHONOLOGY</th><th>LAST-1</th><th>LAST-2</th></tr></thead><tbody><tr><td>Blick</td><td>M</td><td>/blik/</td><td>k</td><td>i</td></tr><tr><td>Scheik</td><td>M</td><td>/ʃaɪk/</td><td>k</td><td>i</td></tr><tr><td>Schick</td><td>M</td><td>/ʃɪk/</td><td>k</td><td>i</td></tr><tr><td>Teig</td><td>M</td><td>/taɪk/</td><td>k</td><td>i</td></tr><tr><td>Tick</td><td>M</td><td>/tɪk/</td><td>k</td><td>i</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr></tbody></table>

A model trained directly on this representation would try to learn that if the word ends in /k/ it is likely to be masculine, or if the word has an /i/ in its last-2 position it is likely to be masculine, and so forth. While this approach works well if the patterns at play are very consistent across most items, it can run into problems when the patterns are more local. For example, in our complete data set, out of all nouns that end in /k/, 726

<footnote n="19">¹⁹ Exceptions are TiMBL and to some degree AM, which also use distance-based approaches.</footnote>
are masculine, 317 feminine, and 254 neuter; and for nouns with /i/ in the last-2 position, 664 are masculine, 2,578 feminine, and 543 neuter. A naive model that only considers phonemes at fixed positions will struggle to learn this pattern. Even when looking at both positions at the same time, the number of nouns that end in /ik/ is fifty-seven masculine, 186 feminine, and thirty-seven neuter. However, the pattern becomes clear when one focuses on monosyllables. For monosyllabic nouns ending in /ik/, fifteen are masculine and one is neuter. A model trained on this type of representation (i.e. on individual phonemes as predictors) will struggle to find more local patterns like the one in Table 5.

In this article we present a new technique,<fnref n="20" /> which aims at integrating both the analogical principle and modern machine learning techniques. Instead of representing nouns as strings of phonemes, our model represents the semantics and phonology of nouns as their similarity to their nearest masculine, feminine, and neuter neighbors. In order to calculate the lexical semantic similarity between nouns we use distributional semantics, and for phonological similarities we employ a modified Levenshtein distance metric. The phonological similarities are calculated independently of the semantic similarities. We now turn to the description of these measurements.

**5.3. MEASURING SEMANTIC SIMILARITY.** Distributional semantics is grounded in Harris's (1954) and Firth's (1957) hypothesis that the meaning of a word is given by the neighbors it appears with. We can assume that '[t]he degree of semantic similarity between two linguistic expressions A and B is a function of the similarity of the linguistic contexts in which A and B can appear' (Lenci 2008:2, 2018). Table 6 shows a toy example of the distribution of three nouns in an imaginary corpus: *apple*, *orange*, and *tomato* in relation to the verbs *cook* and *peel*.

<table><caption>TABLE 6. Distribution of three nouns: *apple*, *orange*, and *tomato*.</caption><thead><tr><th></th><th>cook</th><th>peel</th></tr></thead><tbody><tr><td>apple</td><td>1</td><td>5</td></tr><tr><td>orange</td><td>1</td><td>2</td></tr><tr><td>tomato</td><td>4</td><td>0</td></tr></tbody></table>

The idea is that *apple* appears in our imaginary corpus once with *cook* and five times with *peel*, *orange* once with *cook* and twice with *peel*, and *tomato* four times with *cook* and never with *peel*. From this distribution we can represent the meaning of these three words as vectors, with one dimension for *cook* and one dimension for *peel*, as in Figure 3.

<figure><img src="image_8.png" /><figcaption>FIGURE 3. Semantic vectors for *apple*, *orange*, and *tomato*.</figcaption></figure>

<footnote n="20">²⁰ We share our code and data so that others can reproduce our findings and take the analysis further. The material can be found at: https://osf.io/kyqct/.</footnote>
In this imaginary corpus the vectors for *apple* and *orange* are more similar to each other than they are to the vector of *tomato*. Based on the notion that words which have a similar distribution in a corpus have similar meaning, we can use semantic vectors to represent and calculate the semantic distance between words. More formally, the semantic similarity between two words is calculated as the cosine similarity between their vectors (resulting in a number between 0 = maximally distinct and 1 = maximally similar).<fnref n="21" /> In the previous example, the cosine distance similarity between *apple* and *orange* is 0.96, the cosine similarity between *apple* and *tomato* is 0.2, and the cosine similarity between *orange* and *tomato* is 0.45. While this is just a toy example, it illustrates how semantic vectors help us measure the semantic similarities between lexemes. In practice, distributional approaches to semantics have proven to be extremely useful in linguistic research (Turney 2012, Marelli & Baroni 2015, Padó et al. 2016, Amenta et al. 2020, Wauquier 2020, Wauquier et al. 2020, Guzmán Naranjo & Bonami 2021a, Varvara et al. 2021). For a comprehensive overview of work on linguistic questions using distributional semantics, see Boleda 2020.

For building the semantic vectors we use the DECOW16b corpus (Schäfer & Bildhauer 2012, Schäfer 2015), which contains around 11 billion words. Instead of counting cooccurrences, we use a predictive approach to building the semantic vectors (Baroni, Dinu, & Kruszewsk 2014, Mikolov et al. 2013). In a predictive approach, we fit a neural network that tries to predict each word from its context (or the other way around). By doing this, the neural network builds an internal representation of each word, which we can use as our distributional vector. To build our vectors we use Word-2Vec (Mikolov et al. 2013).<fnref n="22" /> Our vectors have 300 dimensions instead of the two in the toy example.<fnref n="23" /> A property of semantic vectors is that they capture all distributional information found in a corpus, which includes lexical semantics but can also include other information. Building semantic vectors from a raw corpus can lead to a situation where the vectors learn the gender of a noun from the agreeing targets, which could undermine the claim that the semantic properties of nouns help determine gender. To avoid this situation, we carefully neutralized the corpus for gender (see Veeman 2020 for an example of why gender-neutralizing the corpus is important). We first extracted the lemmas provided by the DECOW16b corpus. The lemmatization provided by the DECOW16b corpus already removes most gender information like adjective inflection, but still contains some agreement targets, which we deleted. For example, the lemmatization found in the corpus still distinguishes between *der*, *die*, and *das*, and it does not neutralize preposition + article combinations (like *im*), which still contain gender information. We took care to neutralize all elements of this type (including pronouns, articles, relative pronouns, etc.) from the corpus (we coded all articles as ART, split elements like *im* into *in* + ART, etc.). We built vectors for all of the nouns in our data set, with the exception of 196 nouns that were not found in the DECOW16b corpus.

It is important to emphasize that degendering the corpus is a crucial step for the task of predicting gender from semantic vectors. If we were to use a non-gender-neutralized

<footnote n="21">²¹ The cosine similarity ignores the magnitude of the vectors and takes only the angle between them into account. This is because we want to normalize for words of different frequencies.</footnote>

<footnote n="22">²² Note that Word2Vec does not access word-internal structure, which means that the semantic representations do not know about the morphological or phonological makeup of the words. This is very desirable in the context of this study. Vector architectures like FastText are not a viable alternative in our case because they include information about the sublexical (i.e. phonological) structure of words.</footnote>

<footnote n="23">²³ For our vectors we used a window of six words, five iterations, ten noise words for negative sampling, and a vector size of 300. We did not discard any low-frequency words from the corpus. We built the vectors using part-of-speech information.</footnote>
corpus, the semantic representation of a word like *Mann* would contain the information that it often occurs with masculine articles, demonstratives, and agreeing adjectives, and that a word like *Frau* often occurs with feminine articles, demonstratives, and agreeing adjectives. Making predictions based on this information is different from trying to make predictions based on the lexical semantic properties of nouns. It is worth mentioning that, as far as we know, all previous studies on gender assignment using semantic vectors have not taken the issue of degendering the corpus into account. Works like Williams et al. 2009 and Basirat et al. 2021 use vectors that conflate the lexical semantics and agreement patterns of nouns. This is a methodological problem because it means that the models' predictions will not be based solely on the lexical semantics of the nouns, but will also be influenced by the agreement patterns with which the nouns occur. In other words, they conflate the two sides of the issue, agreement and assignment (see §2.1).

Having built the vectors, we calculated the cosine distance of every noun to its five nearest masculine, five nearest feminine, and five nearest neuter neighbors (thus fifteen nearest neighbors for each noun).<fnref n="24" /> For the 196 nouns not found in the DECOW16b corpus, we assume that they are equidistant to all other nouns (i.e. the model has no lexical semantic information about these). For example, our semantic representation of *Ritter* 'knight', *Birne* 'pear', and *Auto* 'car' would be as given in Table 7 (for reasons of space we give only three neighbors). We then take the distance to each of these fifteen neighbors as a predictor in the model. In other words, the semantic component based on the semantic vectors consists of fifteen numeric predictors that contain the distance of each noun to the nearest five masculine, five feminine, and five neuter neighbors. The rationale behind this decision is that it is important to know not only what the nearest semantic neighbors of a noun are, but also how similar a noun is to its neighbors. This is captured by our representation. The examples in Table 7 show how the predictors would look (despite showing only nine neighbors instead of fifteen).

<table><caption>TABLE 7. Example lexemes and their three nearest semantic neighbors (per gender).</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Ritter 'knight'</th><th colspan="3">EXAMPLE LEXEMES</th><th rowspan="2">Auto 'car'</th></tr><tr><th>Birne 'pear'</th><th></th><th></th></tr></thead><tbody><tr><td>M_1</td><td>Krieger 'warrior'</td><td>0.153</td><td>Apfel</td><td>'apple'</td><td>0.116</td><td>Motorroller 'scooter'</td><td>0.188</td></tr><tr><td>M_2</td><td>König 'king'</td><td>0.183</td><td>Kürbis</td><td>'pumpkin'</td><td>0.18</td><td>Sportwagen 'sports car'</td><td>0.213</td></tr><tr><td>M_3</td><td>Schurke 'villain'</td><td>0.187</td><td>Trüffel</td><td>'truffle'</td><td>0.225</td><td>Schlitten 'sled'</td><td>0.214</td></tr><tr><td>N_1</td><td>Schwert 'sword'</td><td>0.214</td><td>Äpfelchen</td><td>'little apple'</td><td>0.18</td><td>Motorrad 'motorbike'</td><td>0.122</td></tr><tr><td>N_2</td><td>Einhorn 'unicorn'</td><td>0.216</td><td>Früchtchen</td><td>'small fruit'</td><td>0.22</td><td>Fahrrad 'bicycle'</td><td>0.15</td></tr><tr><td>N_3</td><td>Schild 'sign'</td><td>0.237</td><td>Milchbrötchen</td><td>'milk bun'</td><td>0.22</td><td>Moped 'moped'</td><td>0.156</td></tr><tr><td>F_1</td><td>Zauberin 'sorceress'</td><td>0.21</td><td>Zwetschge</td><td>'plum'</td><td>0.15</td><td>Straßenbahn 'tram'</td><td>0.191</td></tr><tr><td>F_2</td><td>Barde 'bard'</td><td>0.21</td><td>Möhre</td><td>'carrot'</td><td>0.2</td><td>Klimaanlage 'air conditioning'</td><td>0.243</td></tr><tr><td>F_3</td><td>Reiterei 'cavalry'</td><td>0.23</td><td>Mirabelle</td><td>'yellow plum'</td><td>0.2</td><td>Langstrecke 'long-distance route'</td><td>0.248</td></tr></tbody></table>

These examples show that nearest neighbors tend to belong to the same gender. This is a statistical generalization, not an absolute; hence we find *Birne* (F) 'pear', whose nearest neighbor is *Apfel* (M) 'apple'. It is also worth pointing out that occasionally neighbors are lexically not similar, but the vectors have similar representations because

<footnote n="24">²⁴ We tested different numbers of nearest neighbors, and this number yielded consistently good results.</footnote>
they tend to appear in very similar contexts, like *Auto* ‘car’ and *Langstrecke* ‘long-distance route’.

5.4. MEASURING PHONOLOGICAL SIMILARITY. In our approach to phonological distance, we use right-edge weighted Levenshtein distances. We assume that for German similarity on the right-hand side is more important than similarity on the left-hand side. We count the differences between two strings, but weight each difference by its distance from the right-hand side edge. For instance, *Baum* /baom/ ‘tree’ and *Bauch* /baux/ ‘belly’ differ in exactly one segment, and that segment is the first one counting from the right, resulting in a distance of 1 (1/1 = 1). The nouns *Baum* /baom/ and *Saum* /zaom/ ‘seam’ also differ in exactly one segment, but the segment is the fourth counting from the right, thus yielding a distance of 0.25 (1/4 = 0.25). (More detail on this measurement is provided in the appendix.) This approach of weighting the distance from the right edge of the word works better, based on multiple experiments (Guzmán Naranjo & Bonami 2021b, Bonami & Pellegrini 2022). Thus, we build a representation of phonology analogous to the one described above for semantics. We also calculate the phonological distance of each noun to its nearest five masculine, five feminine, and five neuter neighbors, and use these distances as the phonological information. It is worth noting that the phonological and semantic neighbors are estimated independently of each other and do not need to overlap.

5.5. MORPHOLOGICAL INFORMATION (DERIVATION, INFLECTION CLASS) AND LEXICAL SEMANTIC INFORMATION (ANIMACY, SEX, ETC.). Apart from the distance-based measures we also used categorical information. For morphological derivational processes we use the most peripheral derivational suffix and the most peripheral derivational prefix on the noun. While the CELEX database provides the morphological structure of the nouns, no distinction is made between derivation and compounding, and while CELEX offers segmentation of morphologically complex forms, the constituent parts are not labeled as stem or affixes. For this reason, we manually reannotated all complex nouns in our database, coding them as compounds or derivations, and for the latter noting the last derivation process. This process left us with forty-three prefixes and 112 suffixes. Additionally, for prefixes, we annotated whether the element is a particle (*aus*-, e.g. *Ausreißer* [[[aus][reiss]]er] ‘runaway’),<fnref n="25" /> an embedded prefix (*be*-, e.g. *Besucher* [[[be]such]er] ‘visitor’), or an unembedded prefix (*erz*-, e.g. *Erzfeind* [[erz][feind]] ‘archenemy’). For inflection class information we take the same approach. Inflection class is annotated in the CELEX database as a singular-plural combination, but it was missing for a considerable number of nouns. We manually corrected and completed the annotations. This leaves us with 188 inflection classes.<fnref n="26" /> Finally, apart from semantic distances, we also used hand-annotated lexical semantics consisting of four features: animacy (with the values human, animate, inanimate), concreteness, that is, whether the referent can be perceived with one of the five senses (yes, no), sex (male, female, epicene, none), and mass, that is, whether any part of the referent can be described with the same noun

<footnote n="25">²⁵ German distinguishes prefixes from particles in verb formation. Particles have main stress and allow for insertion of *ge-* to construct the participle, while prefixes do not. Particles are also syntactically separable, while prefixes are not (Fleischer & Barz 2012).</footnote>

<footnote n="26">²⁶ The relatively large number is because the data set contains many borrowings with very idiosyncratic inflection classes with small membership. However, as noted earlier, there are also many loanwords in the larger inflection classes. It is therefore logical to work from the inflection class, since being a loanword is not in itself a predictor of gender.</footnote>
(yes, no, indeterminate). For example, *Ritter* 'knight' has the specification [animacy: human; concrete: yes; sex: male; mass: no], *Birne* 'pear' has the values [animacy: inanimate; concrete: yes; sex: none; mass: no], and *Auto* 'car' has the values [animacy: inanimate; concrete: yes; sex: none; mass: no]. We treat these predictors as categorical.

**5.6. SUMMING UP.** Our predictors are: lexical semantics (sex, animacy, concreteness, mass), distributional semantics (represented as distances to the five nearest masculine, the five nearest feminine, and the five nearest neuter nouns), phonology (represented as distances to the five nearest masculine, the five nearest feminine, and the five nearest neuter nouns), derivational predictors (suffix or prefix), and inflection. In the remainder of the article, we use the abbreviations SEM (distances), SEM (lexical), PHON, DERIV, and INFL. In some instances, we combined SEM (distances) and SEM (lexical), for which we simply use the abbreviation SEM.

**5.7. AUTOMATIC RULE INDUCTION: BOOSTING TREES.** Instead of finding rules manually, we use a boosting tree model (Chen et al. 2015), which is one of the best classification techniques there is, as demonstrated in similar tasks (Guzmán Naranjo & Bonami 2021a, Bonami & Pellegrini 2022). The intuition of boosting trees is similar to that of RANDOM FORESTS (Breiman 2001). We build many small decision trees, which individually are relatively weak classifiers, and then join them together into a much stronger system that automatically learns all of the rules and interactions in the data. For all predictors and combinations thereof, the model builds many small decision trees. The main difference between boosting trees and random forests is that in a random forest, each individual tree is fitted with a random subset of the predictors and a random subset of the data. In contrast, in boosting trees, each tree is fitted on the residuals of the previous tree, which means that the model iteratively improves on its previous state.

An important issue to bear in mind is overfitting. We want to know how well the different factors help predict the gender of new nouns, and not just how well the model can remember the gender of nouns it has already encountered. Boosting trees are generally very robust against overfitting, and several regularization meta-parameters are used to prevent it. To measure how well our model would perform on new data and to assess whether we are overfitting the data, we perform ten-fold cross-validation. We split our data set into ten groups with roughly the same number of nouns each. We then train the model of interest using nine of the ten groups and predict the remaining group, and then repeat this process for each group. In each iteration, a new model is trained without the information from the previous iteration. This way, the model is always predicting new, unseen nouns. The total accuracy of the model is calculated on the predictions of all ten iterations. One additional aspect that we took into account when building the cross-validation data sets was that we kept all compounds sharing the same noun as the second element in the same cross-validation group as the base noun. For example, all compounds with the noun *Bier* 'beer' as second element: *Altbier*, *Fassbier*, *Flaschenerbier*, and so forth were kept in the same group as the noun *Bier*. The reason for this is the last-member principle (see §3.2 above). If the trained model already saw the word *Bier*, then it would be trivial to predict all of its compounds.

Since we are interested in understanding both how the individual predictors behave and how they work together, we built a series of models with all possible combinations of our predictors. We treat SEM (distances) and SEM (lexical) as independent predictors only in models with single predictors. We combine these two predictors into SEM for all other model combinations. This allows us to compare and evaluate the contribution made by each individual predictor.
**6. RESULTS.** In discussing the results of our models, we begin with overall accuracy figures and variable importance, then talk about the effects of frequency, and finally go into nested generalizations.

**6.1. OVERALL ACCURACY AND VARIABLE IMPORTANCE.** First, we look at the accuracy of each individual model. The x-axis in Figure 4 gives the estimated accuracy, and the y-axis compares models that differ in terms of the information that was given to the model. We present models for all predictor combinations (PHON, DERIV, INFL, and SEM), plus models for just the singular inflection, plural inflection, SEM (lexical), and SEM (distances). The overall accuracy results show clearly that the system is anything but unpredictable. This should not come as a surprise given previous research on German and on gender assignment more generally, but astonishingly the old myth that German gender assignment is somehow arbitrary and unpredictable still gets repeated despite all evidence to the contrary. The German system—while complex—is governed by clear principles. For example, Köpcke and Zubin's system achieves an accuracy of around 75% on simplex nouns and 71% on our whole data set, and Basirat et al. (2021), who do not take morphological information (derivation, inflection class) into account, using a bespoke data set constructed for the purpose of their study, reach an accuracy of 88% using phonology, and 87% with a model that combines phonology and semantics.

<figure><img src="image_9.png" /><figcaption>FIGURE 4. Overall model accuracy.</figcaption></figure>

Our model achieves better results. We assume that there is some fluctuation in the accuracy estimates due to the cross-validation and the random nature of boosting tree classifiers, and we therefore also calculated the 95% uncertainty intervals for the accuracy values, represented by the black whiskers.<fnref n="27" />

The combined factors in our model reach a predictive success of 96%, as the top line in Fig. 4 shows. In fact, the top two models—that is, all four predictors and all

<footnote n="27">²⁷ We are using a binomial Bayesian model. Uncertainty intervals are different in their interpretation from confidence intervals. They measure (as their name indicates) how certain we are about the estimates of interest. We fitted the Bayesian models using Stan (Carpenter et al. 2017) and brms (Bürkner 2017). A full description of the model is provided in the appendix.</footnote>
predictors except derivation—perform equally well.<fnref n="28" /> Derivation is essentially irrelevant once we have the phonology. But there is at least some nonoverlapping information in the other predictors. This is important because it shows that they all contribute. As we noted earlier, the majority case baseline is 45%. This is the accuracy a model would achieve by predicting the most frequent class. Any model with an accuracy over 45% is performing better than a model just predicting feminine. As far as single predictors are concerned, phonology is best. Inflection class is decent; in fact, it is the best predictor for simplex nouns. Predicting from the genitive singular works better than from the nominative plural. Semantics is less good, but works well for humans; and semantics based on distances performs much better than semantics based on lexical meaning. Derived status is relatively weak, but still over the baseline of 45%. The reason for this weak performance is twofold. First, only some suffixes are strongly predictive of gender (for example, -keit or -heit); others are only weakly predictive. Second, only a subset of nouns have a suffix, which means that the prediction for most nouns, when looking at only suffixes, will be the majority class (when the model encounters no information, i.e. no suffix, it assigns an item to the default, i.e. most frequent, class). Thus, the 58% accuracy of derivation is here calculated with respect to the whole corpus, and not with respect to only derived nouns.

We can calculate a variable importance estimate from the overall accuracies (Table 8). Thus, to calculate the variable importance of the phonology we subtract the accuracy of the model without the phonology from the accuracy of the full model: 0.96 - 0.91 = 0.05.

<table><caption>TABLE 8. Variable importance of individual variables.</caption><thead><tr><th>variable to be evaluated</th><th>overall accuracy</th><th>minus</th><th>overall accuracy without variable to be evaluated</th><th>equals</th><th>variable importance</th></tr></thead><tbody><tr><td>PHON</td><td></td><td></td><td>0.91</td><td></td><td>0.05</td></tr><tr><td>INFL</td><td>0.96</td><td>-</td><td>0.935</td><td>=</td><td>0.025</td></tr><tr><td>SEM</td><td></td><td></td><td>0.951</td><td></td><td>0.009</td></tr><tr><td>DERIV</td><td></td><td></td><td>0.959</td><td></td><td>0.001</td></tr></tbody></table>

Doing this for all four predictors, we see that phonology clearly performs best.<fnref n="29" /> ‘Variable importance’ provides a measure of the percentage of nouns that one predictor can cover, so for 5% of nouns we need the phonology, but this does not tell us anything about overlap. There might be many nouns which the phonology predicts correctly that other predictors would also predict correctly.

We might call this ‘additive’ variable importance. We ignore overlap of predictors; multiple predictors can point in the same direction. This is in line with an approach where assignment principles can reinforce each other. Another way of looking at predictors is what we might call ‘subtractive’ variable importance. Here we try to eliminate all redundancy. We include a predictor only if it does something that no other predictor does. This is in line with an approach where assignment principles are set up in a par- simonious way.

<footnote n="28">²⁸ The accuracy of the full model is 0.96, while the accuracy of the model PHON-SEM-INFL is 0.959. The difference between these two numbers is likely to be due to random variation, given the uncertainty intervals (the black whiskers) estimated by the model.</footnote>

<footnote n="29">²⁹ This might appear to go against the typological prediction, but the core semantic rule works very well, for the appropriate nouns, while all nouns are subject to the phonological rules. This helps explain children’s early overuse of phonological predictors mentioned earlier (§3).</footnote>
A look at subtractive variable importance yields the following picture (Table 9).

<table><caption>TABLE 9. Subtractive variable importance based on 30,576 nouns.</caption><thead><tr><th>MODELS</th><th>TOTAL REMAINING</th><th>N NOUNS PREDICTED</th><th>ACCURACY</th></tr></thead><tbody><tr><td>PHON (distances)</td><td>1,673</td><td>1,277</td><td>0.76</td></tr><tr><td>INFL</td><td>834</td><td>438</td><td>0.53</td></tr><tr><td>SEM</td><td>706</td><td>310</td><td>0.44</td></tr><tr><td>DERIV</td><td>495</td><td>99</td><td>0.20</td></tr><tr><td>Köpckian rules</td><td>1,673</td><td>9</td><td>0.005</td></tr></tbody></table>

For each predictor, we see the number of nouns that remain when we remove from the data set all nouns covered by the other predictors, and the number of nouns that the predictor under scrutiny predicts out of the remaining nouns. So the phonology can predict another 1,277 of the 1,673 nouns not correctly predicted by the other predictors. Now we can be sure that we need the phonology for only these 1,277 nouns; all other nouns that the phonology might cover are also covered by other predictors.

For Köpckian-style phonological rules (last row in Table 9), we first run all models except the phonology, which leaves us with 1,673 nouns; the Köpckian rules correctly predict nine additional nouns, which makes them inferior to our distance-based phonology predictor, which correctly predicts another 1,277 nouns. This does not mean that Köpckian-style rules are not valuable, but it shows that their import is indirect by reinforcing other principles. It confirms what Köpcke (1982:113) himself pointed out, namely, that his rules serve as learning and memory aids, rather than as rules conforming to the ideal of parsimoniousness and efficiency.

Finally, we look at the nouns that are not predicted correctly in the complete model. While we cannot be entirely certain about any specific case, we give some probable reasons. There are a total of 1,216 errors in the complete model. Of these, 632 nouns belong to inflection classes that overwhelmingly predict a different gender from the noun in question. For example, the noun *Zimmer* 'room' is neuter, but the model predicts masculine. *Zimmer* also belongs to the inflection class -s/-Ø, which has a majority of masculine nouns (2,996 masculine and 1,172 neuter). It is thus likely that in this case (and others like it) the phonology and semantics were not strong enough for the model to make the right prediction. In the remaining 584 error cases unrelated to inflection class, we find that more than half are borrowed (and rather rare) nouns like *Aeon* 'eon', *Whist* 'whist', or *Zen* 'zen'. These nouns do not necessarily follow common phonological patterns in the rest of the data set, and it is thus not surprising that our model struggles with them.

**6.2. FREQUENCY EFFECTS.** So far, we have shown that our model can reach a very high accuracy when trained on the whole data set. However, speakers do not necessarily know all possible German nouns, especially when they are first acquiring the system. See Heitmeier et al. 2024 for a brief overview of different models of frequency effects in learning. It is thus interesting to ask the question of how many nouns are necessary to reach a good predictive accuracy, and how the accuracy of the model changes with the size of the training data. To explore this question, we trained models on the most frequent 100, 1,000, 5,000, 10,000, 15,000, 20,000, and 25,000 nouns and then predicted the least frequent 5,576 nouns.<fnref n="30" /> The results of this process are shown in Figure 5.

<footnote n="30">³⁰ These are the remaining nouns in the data set (30,576 – 25,000). We use the same testing data set for all models in order to make the predictions completely comparable across data sets.</footnote>
<figure><img src="image_10.png" /><figcaption>FIGURE 5. Accuracies for lower-frequency nouns (complete model).</figcaption></figure>

It is surprising that training the model with only the most frequent 100 nouns is enough to get an accuracy of 83%, which is better than several previous approaches to the problem (recall that Basirat et al. (2021) achieved an accuracy of about 88% with a training set of tens of thousands of nouns). Training with 1,000 nouns provides an accuracy of 94%, and with 5,000 nouns this rises to 95%, after which it does not increase much more. That so few nouns are sufficient to reach high predictive success interestingly mirrors the fact that young children are quite successful at gender assignment, even though they know comparatively few words (see also Szagun et al. 2007:459). This finding is surprising given the widely held hypothesis that frequent items are less good as input for learning gender because they are the most exceptional ones.

**6.3. NESTED GENERALIZATIONS.** Finally, we turn to nested generalizations, where combinations of criteria make a prediction that neither of the individual criteria is capable of making on its own. We start with a simple example of a two-way nested generalization based on the overlap diagram (Figure 6).

<figure><img src="image_11.png" /><figcaption>FIGURE 6. Example of a two-way nested generalization.</figcaption></figure>
Table 10 gives the gender distributions of the two predictors in question: ‘ends in schwa’ and ‘inflection class -s/- (e)n’.

<table><caption>TABLE 10. Gender distributions of predictors ‘ends in schwa’ and ‘inflection class -s/- (e)n’.</caption><thead><tr><th rowspan="2">predictor</th><th rowspan="2">predicts</th><th colspan="3">actual distribution</th></tr><tr><th>MASCULINE</th><th>FEMININE</th><th>NEUTER</th></tr></thead><tbody><tr><td>Ends in schwa</td><td>FEMININE</td><td>557</td><td>4,195</td><td>206</td></tr><tr><td>IC -s/- (e)n</td><td>MASCULINE</td><td>260</td><td>0</td><td>74</td></tr><tr><td>both</td><td>NEUTER</td><td>0</td><td>0</td><td>19</td></tr></tbody></table>

‘Ends in schwa’ predicts feminine, while ‘inflection class -s/- (e)n’ predicts masculine, but both together predict neuter. Coming to the end, there is the point about *Ende*. It is a noun for which by combining all of the pieces of information (inflection class and phonology) we get a single clear and unequivocal gender value, which we would not have been able to predict by looking at the individual predictors. Importantly, nested generalizations are not examples of overlapping information, but rather we can think of them as ‘rule-reversals’.

To get an idea of the importance of nested generalizations in German, we take the full data set and sequentially remove all nouns correctly predicted by the predictors whose interactions we want to examine. In the first row in Table 11 this would be SEM, then INFL. ‘Total remaining’ is the number of nouns not correctly predicted by either SEM or INFL; ‘number of nouns predicted’ is the number of additional nouns predicted by both SEM and INFL together.

<table><caption>TABLE 11. Two-way nested generalizations, based on 30,576 nouns.</caption><thead><tr><th>MODELS</th><th>TOTAL REMAINING</th><th>N NOUNS PREDICTED</th><th>ACCURACY</th></tr></thead><tbody><tr><td>SEM-INFL</td><td>2,626</td><td>947</td><td>0.36</td></tr><tr><td>PHON-INFL</td><td>944</td><td>140</td><td>0.15</td></tr><tr><td>PHON-SEM</td><td>1,161</td><td>155</td><td>0.13</td></tr><tr><td>SEM-DERIV</td><td>5,096</td><td>311</td><td>0.06</td></tr><tr><td>PHON-DERIV</td><td>1,707</td><td>50</td><td>0.03</td></tr><tr><td>DERIV-INFL</td><td>3,184</td><td>21</td><td>0.02</td></tr></tbody></table>

Semantics-inflection class is the most important interaction; it covers approximately 950 nouns not covered by SEM and INFL alone. Two-way nested generalizations are relevant for about 6% of German nouns, so it is not surprising that they do not figure in traditional accounts. Nested generalizations are not to be confused with overlap. We are not saying that only 6% of German nouns can be predicted by alternative rules, but rather that in 6% of nouns we get nesting effects where a combination of two predictors yields a different result from looking at the predictors individually. We tested for three-way nested generalizations as well (Table 12).

<table><caption>TABLE 12. Three-way nested generalizations, based on 30,576 nouns.</caption><thead><tr><th>MODELS</th><th>TOTAL REMAINING</th><th>N NOUNS PREDICTED</th><th>ACCURACY</th></tr></thead><tbody><tr><td>SEM-DERIV-INFL</td><td>1,034</td><td>16</td><td>0.02</td></tr><tr><td>PHON-INFL-SEM</td><td>260</td><td>6</td><td>0.02</td></tr><tr><td>PHON-DERIV-SEM</td><td>594</td><td>12</td><td>0.02</td></tr><tr><td>PHON-DERIV-INFL</td><td>6,622</td><td>8</td><td>0.01</td></tr></tbody></table>

The column ‘total remaining’ shows the number of nouns that were not correctly predicted by the models using one predictor and the models with two-way interactions.
The column ‘number of nouns predicted’ shows how many additional correct predictions the model with the three-way interactions can make from the remaining nouns. So for the first row in the table, we first check SEM, then DERIV, then INFL, then all two-way combinations of these three factors, and each time remove correctly predicted nouns. Remaining are 1,034 nouns, of which the combined model can predict an additional sixteen. Thus, we see little evidence of three-way interactions being relevant for German.

As already mentioned in the methods section, boosting tree models consist of many individual conditional inference trees, which work together to model the data. While visualizing the whole model is difficult, it is helpful to look at a single conditional inference tree. Figure 7 shows a single tree fitted to the data with the same predictors as our larger boosting tree models, but for presentation purposes we limited the tree to a maximum of five levels. Note that binary variables are coded as 0 (not present/no) or 1 (present/yes). In the tree, the main bulk of the work is done by the inflection class -Ø/-(e)n, which is a reliable predictor of feminine gender. The inflection classes -Ø/- and -Ø/stem change + -e are also included in the model, but their effect is less direct and interacts with the effect of phonological neighbors. The phonological distances to the neighbors have a simple interpretation in this tree. For example, we can look at the predictor for the second neuter neighbor: if a noun is closer to it than ~0.747, then it is likely to be neuter, but if its distance is greater than ~0.747, it is likely to be masculine. In this tree, however, this relation takes effect only after all other predictors higher up in the tree have been considered.

7. **CONCLUSIONS.** The EMPIRICAL conclusion is the confirmation that the gender value of a German noun is highly predictable. This will be surprising for many, since German has been taken, from Bloomfield (1933:280) on, as an extreme example of unpredictability. Still better, and more surprisingly, our model performs well given just the most frequent 100 nouns, where we expect the highest degree of irregularity to be located. The gender assignment system is certainly complex, but it represents a typologically well-known type: a combination of a semantic core and formal (morphological/phonological) assignment principles (Corbett 1991:33–69). A novel perspective is the interlocking regularities of the system, which mutually predict gender and help us to understand how children learn—and adults retain—the system. Our study builds on previous work, most notably Köpcke 1982, which was a major step forward in demonstrating that gender assignment in German is far from arbitrary. Of course, it is no surprise that the methods we use, which have evolved significantly from those available in the 1980s, are more successful in showing the regularities of the system. And we go beyond previous work in offering a holistic account of gender assignment in German, based on a large database and a wide range of predictors. Crucially, we degendered our corpus when determining similarities; this is an essential step to avoid gender information in associated words informing gender prediction (in other words, to avoid the answer being written into the question).

Our METHODOLOGICAL conclusion is that we have demonstrated how typologists can benefit from statistical modeling. We see this in the rigorous testing and checking of proposed generalizations, and in the freedom to experiment with relatively little cost in time and effort, once the database has been carefully set up and checked. Partly as a result of this, we see a shift toward the study of interlocking regularities, as noted above.
<figure><img src="image_12.png" /><figcaption>FIGURE 7. Visualization of predictor interaction in a single tree.</figcaption></figure>

Thus, in earlier work on gender assignment, there was an emphasis on distinguishing regularities from each other, to establish which was responsible for a particular assignment. Once a predictor had been identified, the noun was put aside as accounted for and no further predictors were evaluated with respect to this noun. The current work
takes a broader view of interconnected and mutually reinforcing regularities. For this, German again proves itself a typological gem.

This point also suggests the SIGNIFICANCE of the study, beyond linguistics. The relationship of the generalizations one to another, which we discussed as additive vs. subtractive (§6.1), is a component of much larger issues. The earlier approach was parsimonious, following the established scientific method, enshrined by Occam. And the justification was that it provided a linguistic model, and that any subsequent psycholinguistic account needed to accomplish at least those results. The approach based on statistical modeling outperforms the parsimonious models (though not reaching full accuracy). However, while we knew exactly how our earlier assignment rules worked, we are less clear about the results from statistical modeling. This is a general core question when we apply machine learning models like the ones described in this article: how do they work internally, and what and how do they relate to what humans do? Our methods are a great tool for linguists and can show us how successful a machine-learning approach based on similarities is, but they do not divulge what is going on in the mind of the speaker when performing gender assignment. So while we present impressive results, we have to be cautious and refrain from overambitious claims.

Now for all of its complexity, German gender is a restricted, well-defined domain with many advantages as an area of study (millions of speakers and well-funded linguistics research centers, to name but two). German gender might prove itself a good testing area to make progress on the key question of what machine learning models are actually learning and to what degree the results we obtain using them relate to what speakers actually do. For example, our model shows that several predictors partially overlap. This raises the question: do speakers pay attention to overlapping predictors, or do they filter them out to arrive at something more akin to the parsimonious model?

In one sense, we have answered our question by providing an account of German gender assignment, arguably the holy grail for linguistic gender research. But in another sense, we have just provided the basic material for the real questions, concerning the competence of speakers and how our models relate to that competence. Our typological gem has not yielded all of its secrets.

## APPENDIX A: DESCRIPTION OF BOOSTING TREES (§4.6)

To illustrate how boosting trees work, Figure A1 presents a toy example. In this task, we want to classify each observation as being either a circle or a square (these would be our toy genders), based on their position on the x-axis and the y-axis (these would be our toy predictors). Boosting trees approach this problem by doing multiple iterations and, at each iteration, building a new classifier to solve part of the problem not solved by previous iterations.

At the first iteration, we build the model F1, which says: if $x < 1$, then circle, otherwise square. This model captures two observations correctly, but makes three errors (in red). At the second iteration we build model F2 to target those observations that were incorrectly classified by F1 (now larger in red in Fig. A1), which says: if $y > 4$ then circle, otherwise square; and we add $F1 + F2$. This combination now makes one mistake (in blue). At the last iteration we build F3 to target the final misclassified observation (now larger in blue in the figure), which says: if $x < 5$ then circle, otherwise square; and we add $F1 + F2 + F3$. This combination covers all cases.

This is a toy example<fnref n="31" /> because in this case F1–F3 are classification trees of only a single node, while in reality each decision tree can have many nodes—but the principle is the same.

<footnote n="31">³¹ This is also a simplification of the mathematics behind gradient boosting trees.</footnote>
<figure><img src="image_13.png" /><figcaption>FIGURE A1. Toy example of boosting trees. Figure reproduced unmodified from Zhang et al. 2018, figure 6. Licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).</figcaption></figure>

## APPENDIX B: BAYESIAN MODEL FOR MEASURING UNCERTAINTY IN THE ACCURACY ESTIMATES

To calculate the uncertainty of the accuracy, we assume that model accuracy is binomially distributed. We organize our data in a table (Table A1), where each row contains a classification model and the number of correct predictions.

<table><caption>TABLE A1. Organization of data with model and number of correct predictions.</caption><thead><tr><th></th><th>ANMODEL</th></tr></thead><tbody><tr><td>PHON</td><td>27,883</td></tr><tr><td>SEM</td><td>20,996</td></tr><tr><td>INFL</td><td>25,298</td></tr><tr><td>...</td><td>...</td></tr></tbody></table>

Then we fit a Bayesian model with the following structure:

Accuracy<sub>j</sub> ~ Binomial(N, p<sub>j</sub>)

logit(p<sub>j</sub>) = beta<sub>j</sub>

beta<sub>j</sub> ~ Normal(0, 2)

N = 30576
where *beta*<sub>j</sub> are the coefficients we are interested in and which represent the accuracy of the analogical model. The last line is our prior for the coefficients, which in this case is wide enough to allow for high accuracies, but narrow enough to discard unlikely values.

We fit essentially the same model for Figs. 5 and 6 but use different *N* values, according to the frequency bandwidth.

APPENDIX C: FURTHER EXAMPLES OF ALIGNMENTS AND DISTANCES

Here we present examples of distance calculations for a few word pairs. These help to illustrate how the system works.

<table><caption>(A1) Kamm ‘comb’ – Mann ‘man’</caption><thead><tr><th>k</th><th>a</th><th>m</th></tr></thead><tbody><tr><td>m</td><td>a</td><td>n</td></tr><tr><td>1/3</td><td>0</td><td>1</td></tr></tbody></table>

Total distance = 1.333...

<table><caption>(A2) Baum ‘tree’ – Bach ‘river’</caption><thead><tr><th>b</th><th>a</th><th>u</th><th>m</th></tr></thead><tbody><tr><td>b</td><td>a</td><td></td><td>x</td></tr><tr><td>0</td><td>0</td><td>1/2</td><td>1</td></tr></tbody></table>

Total distance = 1.5

<table><caption>(A3) Buch ‘book’ – Bauch ‘belly’</caption><thead><tr><th>b</th><th>u</th><th>:</th><th>x</th></tr></thead><tbody><tr><td>b</td><td>a</td><td>u</td><td>x</td></tr><tr><td>0</td><td>1/3</td><td>1/2</td><td>0</td></tr></tbody></table>

Total distance = 0.8333...

REFERENCES

ALBRIGHT, ADAM, and BRUCE HAYES. 2002. Modeling English past tense intuitions with minimal generalization. *Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning* 6.58–69. DOI: 10.3115/1118647.1118654.

AMENTA, SIMONA; FRITZ GÜNTHER; and MARCO MARELLI. 2020. A (distributional) semantic perspective on the processing of morphologically complex words. *The Mental Lexicon* 15.62–78. DOI: 10.1075/ml.00014.ame.

ARNDT-LAPPE, SABINE. 2011. Towards an exemplar-based model of stress in English noun–noun compounds. *Journal of Linguistics* 47.549–85. DOI: 10.1017/S0022226711000028.

ARNDT-LAPPE, SABINE. 2014. Analogy in suffix rivalry: The case of English -ity and -ness. *English Language and Linguistics* 18.497–548. DOI: 10.1017/S136067431400015X.

ASHER, R. E. 1985. *Tamil*. London: Croom Helm. [Reprinted, London: Routledge, 1989.]

AUDRING, JENNY. 2014. Gender as a complex feature. *Language Sciences* 43.5–17. DOI: 10.1016/j.langsci.2013.10.003.

AUDRING, JENNY. 2019. Canonical, complex, complicated? *Grammatical gender and linguistic complexity, vol. 1: General issues and specific studies*, ed. by Francesca Di Garbo, Bruno Olsson, and Bernhard Wälchli, 15–52. Berlin: Language Science. DOI: 10.5281/zenodo.3462756.

AUDRING, JENNY. 2023. Gender systems in Germanic. *The Oxford encyclopedia of Germanic linguistics*, ed. by Sebastian Kürschner and Antje Dammel. Oxford: Oxford University Press. DOI: 10.1093/acrefore/9780199384655.013.953.

AUGST, GERHARD. 1975. *Untersuchungen zum Morpheminventar der deutschen Gegenwartssprache*. (Forschungsberichte des Instituts für deutsche Sprache Mannheim 25.) Tübingen: Gunter Narr. Online: https://ids-pub.bsz-bw.de/frontdoor/index/index/docId/1856.

BAAYEN, R. HARALD; RICHARD PIEPENBROCK; and LEON GULIKERS. 1995. The CELEX lexical database (CD-ROM). Philadelphia: Linguistic Data Consortium, University of Pennsylvania.
BACH, EMMON, and ROBERT T. HARMS. 1972. How do languages get crazy rules? *Linguistic change and generative theory*, ed. by Robert P. Stockwell and Ronald K. S. Macauley, 1–21. Bloomington: Indiana University Press.

BARONI, MARCO; GEORGIANA DINU; and GERMÁN KRUSZEWSK. 2014. Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. *Proceedings of the 52nd annual meeting of the Association for Computational Linguistics (Vol. 1: Long papers)*, 238–47. DOI: 10.3115/v1/P14-1023.

BASIRAT, ALI; MARC ALLASSONNIÈRE-TANG; and ALEKSANDRIS BERDICEVSKIS. 2021. An empirical study on the contribution of formal and semantic features to the grammatical gender of nouns. *Linguistics Vanguard* 7:20200048. DOI: 10.1515/lingvan-2020-0048.

BENIAMINE, SACHA. 2018. *Classifications flexionnelles : Étude quantitative des structures de paradigmes*. Paris: Université Paris Diderot (Paris 7) dissertation.

BENIAMINE, SACHA. 2021. One lexeme, many classes: Inflection class systems as lattices. *One-to-many relations in morphology, syntax, and semantics*, ed. by Berthold Crysmann and Manfred Sailer, 23–51. Berlin: Language Science. DOI: 10.5281/zenodo.4729789.

BINANZER, ANJA 2017. *Genus—Kongruenz und Klassifikation: Evidenzen aus dem Zweitspracherwerb des Deutschen*. Berlin: De Gruyter. DOI: 10.1515/9783110548549.

BIRKENES, MAGNUS BREDER; KLEOPATRA CHRONI; and JÜRIG FLEISCHER. 2014. Genus- und Sexuskongruenz im Neuhochdeutschen: Ergebnisse einer Korpusuntersuchung zur narrativen Prosa des 17. bis 19. Jahrhunderts. *Deutsche Sprache* 42.1–24. DOI: 10.37307/j.1868-775X.2014.01.02.

BITTNER, DAGMAR. 1999. Gender classification and the inflectional system of German nouns. *Gender in grammar and cognition, Part 1: Approaches to gender*, ed. by Barbara Unterbeck, Matti Rissanen, Terttu Nevalainen, and Mirja Saari, 1–23. Berlin: De Gruyter. DOI: 10.1515/9783110802603.1.

BLOOMFIELD, LEONARD. 1933. *Language*. New York: Holt, Rinehart and Winston.

BOLEDA, GEMMA. 2020. Distributional semantics and linguistic theory. *Annual Review of Linguistics* 6.213–34. DOI: 10.1146/annurev-linguistics-011619-030303.

BONAMI, OLIVIER, and SACHA BENIAMINE. 2021. Leaving the stem by itself. *All things morphology: Its independence and its interfaces*, ed. by Sedigheh Moradi, Marcia Haag, Janie Rees-Miller, and Andrija Petrovic, 81–98. Amsterdam: John Benjamins.

BONAMI, OLIVIER; MATÍAS GUZMÁN NARANJO; and DELPHINE TRIBOUT. 2019. The role of morphology in gender assignment in French. Paper presented at the International Symposium of Morphology (ISMo 2019), Paris.

BONAMI, OLIVIER, and MATTEO PELLEGRINI. 2022. Derivation predicting inflection: A quantitative study of the relation between derivation history and inflectional behavior in Latin. *Studies in Language* 46.753–92. DOI: 10.1075/sl.21002.bon.

BRAUN, FRIEDERIKE, and GEOFFREY HAIG. 2010. When are German ‘girls’ feminine? How the semantics of age influences the grammar of gender agreement. *Language in its socio-cultural context: New explorations in gendered, global and media uses*, ed. by Markus Bieswanger, Heiko Motschenbacher, and Susanne Mühleisen, 69–83. Frankfurt am Main: Peter Lang.

BREIMAN, LEO. 2001. Random forests. *Machine Learning* 45.5–32. DOI: 10.1023/A:1010 933404324.

BRESNAN, JOAN; ANNA CUENI; TATIANA NIKITINA; and R. HARALD BAAYEN. 2007. Predicting the dative alternation. *KNAW Academy Colloquium: Cognitive Foundations of Interpretation*, October 27–28, 69–94.

BRESNAN, JOAN, and JENNIFER HAY. 2008. Gradient grammar: An effect of animacy on the syntax of *give* in New Zealand and American English. *Lingua* 118.245–59. DOI: 10.1016/j.lingua.2007.02.007.

BÜRKNER, PAUL-CHRISTIAN. 2017. brms: An R package for Bayesian multilevel models using Stan. *Journal of Statistical Software* 80.1–28. DOI: 10.18637/jss.v080.i01.

BYBEE, JOAN L. 1985. *Morphology: A study of the relation between meaning and form*. Amsterdam: John Benjamins.

BYBEE, JOAN L., and DAN I. SLOBIN. 1982. Rules and schemas in the development and use of the English past tense. *Language* 58.265–89. DOI: 10.1353/lan.1982.0021.

CAHILL, LYNNE, and GERALD GAZDAR. 1999. German noun inflection. *Journal of Linguistics* 35.1–42. DOI: 10.1017/S0022226798007294.
CALLIES, MARCUS. 2015. Effects of cross-linguistic influence in word formation. *Transfer effects in multilingual language development*, ed. by Hagen Peukert, 129–45. Amsterdam: John Benjamins.

CARPENTER, BOB; ANDREW GELMAN; MATTHEW HOFFMAN; DANIEL LEE; BEN GOODRICH; MICHAEL BETANCOURT; MARCUS BRUBAKER; JIQIANG GUO; PETER LI; and ALLEN RIDDELL. 2017. Stan: A probabilistic programming language. *Journal of Statistical Software* 76.1–32. DOI: 10.18637/jsst.v076.i01.

CHEN, TIANQI; TONG HE; MICHAEL BENESTY; VADIM KHOTILOVICH; YUAN TANG; and HYUNSU CHO. 2015. xgboost: Extreme gradient boosting. R package version 0.4-2. Online: https://cran.r-project.org/web/packages/xgboost/.

COPOT, MARIA; TIMOTHEE MICKUS; and OLIVIER BONAMI. 2022. Idiosyncratic frequency as a measure of derivation vs. inflection. *Journal of Language Modelling* 10.193–240. DOI: 10.15398/jlm.v10i2.301.

CORBETT, GREVILLE G. 1986. Gender in German: A bibliography. *Linguistische Berichte* 103.280–86.

CORBETT, GREVILLE G. 1991. *Gender*. Cambridge: Cambridge University Press. DOI: 10.1017/CBO9781139166119.

CORBETT, GREVILLE G. 2009. Canonical inflectional classes. *Selected proceedings of the 6th Décembrettes*, 1–11. Online: https://www.lingref.com/cpp/decemb/6/abstract2231.html.

CORBETT, GREVILLE G. 2012. *Features*. Cambridge: Cambridge University Press. DOI: 10.1017/CBO9781139206983.

CORBETT, GREVILLE G. 2013. Number of genders. *World atlas of language structures online*, ed. by Matthew S. Dryer and Martin Haspelmath. Leipzig: Max Planck Institute for Evolutionary Anthropology. Online: http://wals.info/chapter/30.

CORBETT, GREVILLE G., and SEBASTIAN FEDDEN. 2016. Canonical gender. *Journal of Linguistics* 52.495–531. DOI: 10.1017/S0022226715000195.

CORBETT, GREVILLE G., and NORMAN M. FRASER. 2000. Default genders. *Gender in grammar and cognition* (Trends in linguistics: Studies and monographs 124), ed. by Barbara Unterbeck, Matti Rissanen, Terttu Nevalainen, and Mirja Saari, 55–97. Berlin: Mouton de Gruyter. DOI: 10.1515/9783110802603.55.

CORTEEN, EMMA CHARLOTTE. 2018. *The assignment of grammatical gender in German: Testing optimal gender assignment theory*. Cambridge: University of Cambridge dis- sertation. DOI: 10.17863/CAM.37638.

CULBERTSON, JENNIFER; ANNIE GAGLIARDI; and KENNY SMITH. 2017. Competition be- tween phonological and semantic cues in noun class learning. *Journal of Memory and Language* 92.343–58. DOI: 10.1016/j.jmkl.2016.08.001.

CULBERTSON, JENNIFER; HANNA JARVINEN; FRANCES HAGGARTY; and KENNY SMITH. 2019. Children’s sensitivity to phonological and semantic cues during noun class learning: Evidence for a phonological bias. *Language* 95.268–93. DOI: 10.1353/lan.2019.0031.

DAELEMANS, WALTER; JAKUB ZAVREL; KO VAN DER SLOOT; and ANTAL VAN DEN BOSCH. 1998. *TimBL: Tilburg memory-based learner, version 1.0: Reference guide*. Tilburg: Tilburg University. Online: https://research.tilburguniversity.edu/en/publications/timbl-tilburg-memory-based-learner-version-10-reference-guide.

DURIEUX, GERT; WALTER DAELEMANS; and STEVEN GILLIS. 2000. On the arbitrariness of lexical categories. *Computational Linguistics in the Netherlands 2000*, 19–35. Leiden: Brill.

DYE, MELODY; PETAR MILIN; RICHARD FUTRELL; and MICHAEL RAMSCAR. 2017. A functional theory of gender paradigms. *Morphological paradigms and functions*, ed. by Ferenc Kiefer, James Blevins, and Huba Bartos, 212–39. Leiden: Brill. DOI: 10.1163/9789004342934_011.

DYKSTRA-PRUIM, PENNYLYN. 2003. L2 acquisition of German plurals: How students form them and textbooks teach them. *Die Unterrichtspraxis/Teaching German* 36.43–55. DOI: 10.2307/3531683.

EDDINGTON, DAVID. 2002. Spanish gender assignment in an analogical framework. *Journal of Quantitative Linguistics* 9.49–75. DOI: 10.1076/jqul.9.1.49.8482.

ENGER, HANS-OLAV. 2009. The role of core and non-core semantic rules in gender assign- ment. *Lingua* 119.1281–99. DOI: 10.1016/j.lingua.2009.02.004.
EVANS, NICHOLAS. 2003. *Bininj Gun-wok: A pan-dialectal grammar of Mayali, Kunwinjku and Kune*. 2 vols. Canberra: Australian National University. DOI: 10.15144/PL-541.

EVANS, NICHOLAS; DUNSTAN BROWN; and GREVILLE G. CORBETT. 2002. The semantics of gender in Mayali: Partially parallel systems and formal implementation. *Language* 78.111–55. DOI: 10.1353/lan.2002.0014.

FAHLBUSCH, FABIAN, and DAMARIS NÜBLING. 2014. 'Der Schauinsland' – 'die Mobiliar' – 'das Turm': Das referentielle Genus bei Eigennamen und seine Genese. *Beiträge zur Namensforschung* 49.245–88. Online: https://bnf.winter-verlag.de/article/BNF/2014/3/2.

FEDDEN, SEBASTIAN; JENNY AUDRING; and GREVILLE G. CORBETT. 2026. How do Germans order a drink?—Non-core rules in German gender assignment. *Festschrift for Nicholas D. Evans*, ed. by Alice Gaby and Rachel Nordlinger. Canberra: Pacific Linguistics, to appear.

FIRTH, JOHN R. 1957. Modes of meaning. *Papers in Linguistics, 1934–1951*, 190–215. Oxford: Oxford University Press.

FLEISCHER, WOLFGANG, and IRMHILD BARZ. 2012. *Wortbildung der deutschen Gegenwartssprache*. 4th edn. Berlin: De Gruyter. DOI: 10.1515/9783110256659.

FRASER, NORMAN M., and GREVILLE G. CORBETT. 1995. Gender, animacy, and declensional class assignment: A unified account for Russian. *Yearbook of Morphology* 1994.123–50. DOI: 10.1007/978-94-017-3714-2_5.

GUZMÁN NARANJO, MATÍAS. 2019. *Analogical classification in formal grammar*. Berlin: Language Science. DOI: 10.5281/zenodo.3191824.

GUZMÁN NARANJO, MATÍAS. 2020. Analogy, complexity and predictability in the Russian nominal inflection system. *Morphology* 30.219–62. DOI: 10.1007/s11525-020-09367-1.

GUZMÁN NARANJO, MATÍAS, and LAURA BECKER. 2021. Coding efficiency in nominal inflection: Expectedness and type frequency effects. *Linguistics Vanguard* 7(s3):20190075. DOI: 10.1515/lingvan-2019-0075.

GUZMÁN NARANJO, MATÍAS, and OLIVIER BONAMI. 2021a. Overabundance and inflectional classification: Quantitative evidence from Czech. *Glossa: a journal of general linguistics* 6:88. DOI: 10.5334/gjgl.1626.

GUZMÁN NARANJO, MATÍAS, and OLIVIER BONAMI. 2021b. An analogical approach to the typology of inflectional complexity. Paper presented at the 5th American International Morphology Meeting, The Ohio State University.

HAJIBAYOVA, LALA. 2013. Basic-level categories: A review. *Journal of Information Science* 39.676–87. DOI: 10.1177/0165551513481443.

HARRIS, ZELLIG S. 1954. Distributional structure. *Word* 10.146–62. DOI: 10.1080/00437956.1954.11659520.

HEITMEIER, MARIA; YU-YING CHUANG; SETH D. AXEN; and R. HARALD BAAYEN. 2024. Frequency effects in linear discriminative learning. *Frontiers in Human Neuroscience* 17:1242720. DOI: 10.3389/fnhum.2023.1242720.

HOCKETT, CHARLES F. 1958. *A course in modern linguistics*. New York: Macmillan.

JANDA, LAURA A. 2019. Quantitative perspectives in cognitive linguistics. *Review of Cognitive Linguistics* 17.7–28. DOI: 10.1075/rcl.00024.jan.

KIBRIK, ALEKSANDR E. (ed.) 2001. *Bagvalinskij jazyk: Grammatika: teksty: slovari* [Bagvalal: Grammar, texts, dictionaries], ed. by K. I. Kazenin, E. A. Ljutikova, and S. G. Tatevosov. Moscow: Nasledie.

KÖPCKE, KLAUS-MICHAEL. 1982. *Untersuchungen zum Genussystem der deutschen Gegenwartssprache*. Tübingen: Niemeyer.

KÖPCKE, KLAUS-MICHAEL; KLAUS-UWE PANTHER; and DAVID A. ZUBIN. 2010. Motivating grammatical and conceptual gender agreement in German. *Cognitive foundations of linguistic usage patterns*, ed. by Hans-Jörg Schmid and Susanne Handl, 171–94. Berlin: De Gruyter Mouton. DOI: 10.1515/9783110216035.171.

KÖPCKE, KLAUS-MICHAEL, and DAVID A. ZUBIN. 1983. Die kognitive Organisation der Genuszuweisung zu den einsilbigen Nomen der deutschen Gegenwartssprache. *Zeitschrift für germanistische Linguistik* 11.166–82. DOI: 10.1515/zfgl.1983.11.2.166.

KÖPCKE, KLAUS-MICHAEL, and DAVID A. ZUBIN. 1984. Sechs Prinzipien für die Genuszuweisung im Deutschen: Ein Beitrag zur natürlichen Klassifikation. *Linguistische Berichte* 93.26–50.
KÖPCKE, KLAUS-MICHAEL, and DAVID A. ZUBIN. 1996. Prinzipien für Genuszuweisung im Deutschen. *Deutsch-typologisch* (Jahrbuch des Instituts für Deutsche Sprache 1995), ed. by Ewald Lang and Gisela Zifonun, 473–91. Berlin: De Gruyter. DOI: 10.1515/9783110622522-021.

KÖPCKE, KLAUS-MICHAEL, and DAVID A. ZUBIN. 2005. Nominalphrasen ohne lexikalischen Kopf—Zur Bedeutung des Genus für die Organisation des mentalen Lexikons am Beispiel der Autobezeichnungen im Deutschen. *Zeitschrift für Sprachwissenschaft* 24.93–122. DOI: 10.1515/zfsw.2005.24.1.93.

KÖPCKE, KLAUS-MICHAEL, and DAVID A. ZUBIN. 2009. Genus. *Deutsche Morphologie*, ed. by Elke Hentschel and Petra M. Vogel, 132–54. Berlin: De Gruyter. DOI: 10.1515/9783110212617.113.

KRIFKA, MANFRED. 2009. Case syncretism in German feminines: Typological, functional and structural aspects. *On inflection*, ed. by Patrick O. Steinkrüger and Manfred Krifka, 141–72. Berlin: Mouton de Gruyter. DOI: 10.1515/9783110198973.141.

KUPISCH, TANJA; MIRIAM GEISS; NATALIA MITROFANOVA; and MARIT WESTERGAARD. 2022. Structural and phonological cues for gender assignment in monolingual and bilingual children acquiring German. *Glossa: a journal of general linguistics* 7(1). DOI: 10.16995/glossa.5696.

KUPISCH, TANJA; NATALIA MITROFANOVA; and MARIT WESTERGAARD. 2022. Phonological vs. natural gender cues in the acquisition of German by simultaneous and sequential bilinguals (German-Russian). *Journal of Child Language* 49(4).661–83. DOI: 10.1017/S0305000921000039.

KÜRSCHNER, SEBASTIAN, and DAMARIS NÜBLING. 2011. The interaction of gender and decline in Germanic languages. *Folia Linguistica* 45.355–88. DOI: 10.1515/flin.2011.014.

LENCI, ALESSANDRO. 2008. Distributional semantics in linguistic and cognitive research. *Rivista di Linguistica* 20.1–31. Online: https://www.italian-journal-linguistics.com/app/uploads/2021/05/1_Lenci.pdf.

LENCI, ALESSANDRO. 2018. Distributional models of word meaning. *Annual Review of Linguistics* 4.151–71. DOI: 10.1146/annurev-linguistics-030514-125254.

LEVSHINA, NATALIA. 2016. When variables align: A Bayesian multinomial mixed-effects model of English permissive constructions. *Cognitive Linguistics* 27.235–68. DOI: 10.1515/cog-2015-0054.

LEVSHINA, NATALIA. 2022. Corpus-based typology: Applications, challenges and some solutions. *Linguistic Typology* 26.129–60. DOI: 10.1515/lingty-2020-0118.

MALKIEL, YAKOV. 1978. Derivational categories. *Universals of human language: Word structure*, ed. by Joseph H. Greenberg, Charles A. Ferguson, and Edith A. Moravcsik, 125–49. Stanford, CA: Stanford University Press.

MARELLI, MARCO, and MARCO BARONI. 2015. Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics. *Psychological Review* 122.485–515. DOI: 10.1037/a0039267.

MATTHEWS, CLIVE A. 2005. French gender attribution on the basis of similarity: A comparison between AM and connectionist models. *Journal of Quantitative Linguistics* 12.262–96. DOI: 10.1080/09296170500055384.

MATTHEWS, CLIVE A. 2010. On the nature of phonological cues in the acquisition of French gender categories: Evidence from instance-based learning models. *Lingua* 120.879–900. DOI: 10.1016/j.lingua.2009.06.007.

MIKOLOV, TOMAS; KAI CHEN; GREG CORRADO; and JEFFREY DEAN. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]. DOI: 10.48550/arXiv.1301.3781.

MILLS, ANNE E. 1986. *The acquisition of gender: A study of English and German*. Berlin: Springer. DOI: 10.1007/978-3-642-71362-0.

NASTASE, VIVI, and MARIUS POPESCU. 2009. What's in a name? In some languages, grammatical gender. *Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP)*, 1368–77. Online: https://aclanthology.org/D09-1142/.

NESSET, TORE. 2003. Gender assignment in Ukrainian: Language specific rules and universal principles. *Poljarnyj Vestnik* 6.71–85. DOI: 10.7557/6.1345.
OPITZ, ANDREAS, and THOMAS PECHMANN. 2016. Gender features in German: Evidence for underspecification. *The Mental Lexicon* 11.216–41. DOI: 10.1075/ml.11.2.03opi.

PADÓ, SEBASTIAN; AURÉLIE HERBELOT; MAX KISSELEW; and JAN ŠNAJDER. 2016. Predictability of distributional semantics in derivational word formation. *Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics*, Technical Papers, 1285–96. Online: https://aclanthology.org/C16-1122/.

PARKER, E. M., and R. J. HAYWARD. 1985. *An Afar-English-French dictionary (with grammatical notes in English)*. London: SOAS, University of London.

PAVLOV, VLADIMIR. 1995. *Die Deklination der deutschen Substantive: Synchronie und Diachronie*. Frankfurt am Main: Peter Lang.

RICE, CURT. 2006. Optimizing gender. *Lingua* 116.1394–1417. DOI: 10.1016/j.lingua.2004.06.013.

ROSCH, ELEANOR; CAROLYN MERVIS; WAYNE GRAY; DAVID JOHNSON; and PENNY BOYES-BRAEM. 1976. Basic objects in natural categories. *Cognitive Psychology* 8.382–439. DOI: 10.1016/0010-0285(76)90013-X.

SÁ-LEITE, ANA RITA; KARLOS LUNA; ÂNGELA TOMAZ; ISABEL FRAGA; and MONTSERRAT COMESAÑA. 2022. The mechanisms underlying grammatical gender selection in language production: A meta-analysis of the gender congruency effect. *Cognition* 224: 105060. DOI: 10.1016/j.cognition.2022.105060.

SCHÄFER, ROLAND. 2015. Processing and querying large web corpora with the COW14 architecture. *Proceedings of the 3rd Workshop on Challenges in the Management of Large Corpora (CMLC-3)*, 28–34. Online: https://ids-pub.bsz-bw.de/frontdoor/index/index/docId/3836.

SCHÄFER, ROLAND. 2018. *Einführung in die grammatische Beschreibung des Deutschen*. 3rd edn. Berlin: Language Science. DOI: 10.5281/zenodo.1421660.

SCHÄFER, ROLAND, and FELIX BILDHAUER. 2012. Building large corpora from the web using a new efficient tool chain. *Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012)*, 486–93. Online: https://aclanthology.org/L12-1497/.

SCHUHMANN, KATHARINA S., and LAURA CATHARINE SMITH. 2022. Practical prosody: New hope for teaching German plurals. *Die Unterrichtspraxis/Teaching German* 55.1–24. DOI: 10.1111/tger.12192.

SCHWICHTENBERG, BEATE, and NIELS O. SCHILLER. 2004. Semantic gender assignment regularities in German. *Brain and Language* 90.326–37. DOI: 10.1016/S0093-934X(03)00445-0.

SKOUSEN, ROYAL. 1989. *Analogical modeling of language*. Dordrecht: Kluwer. DOI: 10.1007/978-94-009-1906-8.

SÖNNING, LUKAS, and VALENTIN WERNER. 2021. The replication crisis, scientific revolutions, and linguistics. *Linguistics* 59.1179–1206. DOI: 10.1515/ling-2019-0045.

STEINMETZ, DONALD. 2006. Gender shifts in Germanic and Slavic: Semantic motivation for neuter? *Lingua* 116.1418–40. DOI: 10.1016/j.lingua.2004.06.014.

STUMP, GREGORY. 2016. *Inflectional paradigms: Content and form at the syntax-morphology interface*. Cambridge: Cambridge University Press. DOI: 10.1017/CBO9781316105290.

SYLVAIN, KAHANE; ZIQIAN PENG; and KIM GERDES. 2023. Word order flexibility: A typometric study. *Proceedings of the Seventh International Conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023)*, 68–80. Online: https://aclanthology.org/2023.depling-1.7/.

SZAGUN, GISELA; BARBARA STUMPER; NINA SONDAG; and MELANIE FRANIK. 2007. The acquisition of gender marking by young German-speaking children: Evidence for learning guided by phonological regularities. *Journal of Child Language* 34.445–71. DOI: 10.1017/S0305000906007951.

TALAMO, LUIGI, and ANNEMARIE VERKERK. 2022. A new methodology for an old problem: A corpus-based typology of adnominal word order in European languages. *Italian Journal of Linguistics* 34.171–226. Online: https://www.italian-journal-linguistics.com/app/uploads/2023/01/8-Talamo.pdf.

THORNTON, ANNA M. 2009. Constraining gender assignment rules. *Language Sciences* 31.14–32. DOI: 10.1016/j.langsci.2007.09.003.
THORNTON, ANNA M. 2019. Overabundance: A canonical typology. *Competition in inflection and word-formation* (Studies in morphology 5), ed. by Franz Rainer, Francesco Gardani, Wolfgang U. Dressler, and Hans Christian Luschützky, 223–58. Cham: Springer. DOI: 10.1007/978-3-030-02550-2_9.

TURNEY, PETER D. 2012. Domain and function: A dual-space model of semantic relations and compositions. *Journal of Artificial Intelligence Research* 44.533–85. DOI: 10.1613/jair.3640.

TVERSKY, BARBARA. 1986. Components and categorization. *Categorization and noun classification*, ed. by Colette Craig, 63–76. Amsterdam: John Benjamins.

VARVARA, ROSSELLA; GABRIELLA LAPESA; and SEBASTIAN PADÓ. 2021. Grounding semantic transparency in context: A distributional semantic study on German event nominalizations. *Morphology* 31.409–46. DOI: 10.1007/s11525-021-09382-w.

VEEMAN, HARTGER. 2020. A comparative study of the grammatical gender systems of languages by means of analysing word embeddings. Uppsala: Uppsala University MA thesis. Online: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425635.

WALTER, ANNIE; TOM FRITZSCHE; and BARBARA HÖHLE. 2021. Grammatical gender acquisition in German: Three-year-old children use phonological cues to learn the gender of novel nouns. *Proceedings of the Boston University Conference on Language Development (BUCLD)* 45.746–60. Online: http://www.lingref.com/bucld/45/BUCLD45-56.pdf.

WAUQUIER, MARINE. 2020. *Confrontation des procédés dérivationnels et des catégories sémantiques dans les modèles distributionnels*. Toulouse: University of Toulouse 2 dissertation. Online: https://theses.hal.science/tel-03543115/.

WAUQUIER, MARINE; NABIL HATHOUT; and CÉCILE FABRE. 2020. Semantic discrimination of technicality in French nominalizations. *Zeitschrift für Wortbildung/Journal of Word Formation* 4.100–119. DOI: 10.3726/zwjw.2020.02.06.

WILLIAMS, ADINA; RYAN COTTERELL; LAWRENCE WOLF-SONKIN; DAMIÁN BLASI; and HANNA WALLACH. 2019. Quantifying the semantic core of gender systems. *Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*, 5734–39. DOI: 10.18653/v1/D19-1577.

WINTER, BODO. 2022. Mapping the landscape of exploratory and confirmatory data analysis in linguistics. *Data analytics in cognitive linguistics: Methods and insights*, ed. by Dennis Tay and Molly Xie Pan, 13–48. Berlin: De Gruyter Mouton. DOI: 10.1515/9783110687279-002.

WU, SHIJIE; RYAN COTTERELL; and TIMOTHY O'DONNELL. 2019. Morphological irregularity correlates with frequency. *Proceedings of the 57th annual meeting of the Association for Computational Linguistics*, 5117–26. DOI: 10.18653/v1/P19-1505.

ZHANG, ZHONGXING; GEERT MAYER; YVES DAUVILLIERS; GIUSEPPE PLAZZI; FABIO PIZZA; ROLF FRONCZEK; JOAN SANTAMARIA; MARKKU PARTINEN; SEBASTIAAN OVEREEM; ROSA PERAITA-ADRADOS; ET AL. 2018. Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning. *Scientific Reports* 8:10628. DOI: 10.1038/s41598-018-28840-w.

ZUBIN, DAVID A., and KLAUS-MICHAEL KÖPCKE. 1984. Affect classification in the German gender system. *Lingua* 63.41–96. DOI: 10.1016/0024-3841(84)90031-7.

[sebastian.fedden@sorbonne-nouvelle.fr] (Fedden)

[mguzmann89@gmail.com] (Guzmán Naranjo)

[g.corbett@surrey.ac.uk] (Corbett)

[Received 4 March 2022; revision invited 27 August 2022; revision received 26 May 2023; revision invited 12 March 2024; revision received 12 April 2024; accepted 4 October 2024]