Go here for a simple listing of the gender rules in german

in german there are 3 genders: masculine, feminine, neutral, der; die; das. You need to memorize which words are which gender, since it affects certain constructions. The accusative, dative forms, for instance, can be different according to the noun’s gender.

A quick example, with accusative:

Er trinkt ein Bier: Bier is neutral.

Er trinkt einen Kaffee Kaffee is masculine. Notice the -en.

There is no escape: you need to learn — or guess — the noun’s gender.

It gets easier with a combination of time and some rule-of-thumb laws, or heuristics.

A heuristic is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect or rational, but which is nevertheless sufficient for reaching an immediate, short-term goal.

Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision 1.

Mental shortcuts to ease the cognitive load… sounds like something useful to learn a language! (all those pesky cases, vocabulary and pronunciation already take a lot of my cognitive load). Let’s see how good those heuristics are and if we can come up with easier ones that apply in most cases.

The data

We have at our disposal a list of the 500.000 most frequent words 2 in german and their genders The file is a csv without a header and begins like this:

word gender
ich n
ist n
es n
was m
ich n
ist n
es n
was m
wie n
ja n
so m
aber n
haben n
da n
nein n
noch m
habe f
hast f
aus n
gut n
jetzt n
sein n
mal n
als m
oder f
nichts n
wo n
werden n
mehr n
warum n
bitte f
etwas n
muss n
los n

The first lines of the data. Genders are shorthanded as m, f, n.

https://github.com/mejutoco/german-grammar-statistics

Textbook heuristics: the rules of the game

We want a rule that:

  • can be applied mechanically (no countries are masculine and similar rules. We want a simple rule, not more things to memorize!) 3 Examples are things like boat names are feminine.
  • Also not interested in learning exceptions. We want the simplest mechanical rule that guesses right the most. We will ignore all the rules that are not mechanical (names of ships are feminine… ok), since we do not want to teach a program what is a ship and what not.
Singular form ending in    
Masculine Feminine Neutral
-ich -ei -chen
-ig -in -lein
-ling -heit -icht
-s -keit -tel
-and -schaft -tum
-ant -ung -eau
-är -a -ett
-ast -ade -in
-eur/-ör -age -ing
-(i)ent -aise/-äse -(i)um
-ier -aille -ma
-iker -ance -ment
-ismus -äne  
-ist -anz  
-or -elle  
  -ence  
  -ette  
  -euse  
  -ie  
  -(i)enz  
  -(i)ere  
  -ik  
  -ille  
  -ine  
  -ion/-ation  
  -isse  
  -(i)tät  
  -itis  
  -ive  
  -ose  
  -sis/-se  
  -ur  
  -üre  

Textbook rules 4


Distribution of genders

Because of this we cannot rely on averages to summarize (in fact, averages are often, like piecharts, a bad idea to analyse data). It is a good idea to look at the distribution of the data.

image

Based on the 500.000 most common german words

The baseline: textbook heuristics

Let’s apply the heuristic rules of our grammar book to our data and see how accurate they are. We will guess the gender according to the textbook rules:

Some rules are pretty short (ending in a single letter). Are there internal contradictions?

We can see that some words match several of the rules at the same time!: Some examples of multiple matches are:

  • -in can be both feminine and neutral Automatically any word ending in -in, according to the textbook heuristics is probably feminine. But it is also probably neutral (see table above), since it has exactly the same rule! We will ignore this -in ending rule

  • -itis (feminine ending), -s (masculine ending) Both match

Whenever there are multiple matches we will match the longest one (in number of characters) only. Once we apply this we get rid of cases with multiple matches

image

Who is bad at grammar now

We can see now that the distribution is similar to our first chart (figure 1) There are more feminine nouns, followed by masculine and neutral. That huge last bar is a problem, though: a lot of the words did not match any rule!

No match

Our only problem now is we still have a lot of words without any heuristic we can apply. This is not good for our cognitive load, remember? We will deal with this in a second, but first, how accurate are the results that do match?

Accuracy

How accurate is the classification of the words that matched a rule? Let’s find out:

image

Very good accuracy

This will be our baseline for, you guessed it, our quest to find a better rule.


Finding a better rule

Groups of n characters

Is there a group of n characters in a noun that correspond to a particular gender?

We will test all the permutations from 2 to 4 letters containing, starting or ending in the string.

All 4 character permutations of the 30 letters a b c d e f g h i j k l m n o p q r s t u v w x y z ß ü ö ä means we are dealing with = 657,720 permutations.

We will discard groups that match less than 1% of the time, to avoid having a huge list of values. We are looking at words that start, end , or contain each group of characters.

An inefficent implementation, and 21 hours later the program has finished. What are the results?

Removing rules inconsistency

Some rules match all genders. 5 We remove these duplicates and get our data to start looking for useful rules.

Here is the full table, ordered by Gender matched, Percent matched, Match type and Num matched:

Gender matched Value matched Num matched Percent matched Match type
n en 2819 0.35746893228506216 contain
n er 2254 0.28582297742835405 contain
n en 1874 0.23763631752472736 end
n ch 1820 0.2307887395384225 contain
n te 1228 0.15571899568856198 contain
n ge 1058 0.13416180573167638 contain
n ei 1045 0.13251331473497338 contain
n re 918 0.11640882576718235 contain
n el 913 0.11577479076845044 contain
n in 908 0.1151407557697185 contain
n sc 898 0.11387268577225462 contain
n sch 890 0.11285822977428354 contain
n st 885 0.1122241947755516 contain
n ie 877 0.11120973877758053 contain
n he 785 0.09954349480091301 contain
n au 716 0.09079381181841238 contain
n an 671 0.08508749682982501 contain
n se 664 0.08419984783160031 contain
n le 627 0.07950798884098402 contain
n be 621 0.07874714684250571 contain
n de 586 0.0743089018513822 contain
n ra 585 0.07418209485163581 contain
n er 577 0.07316763885366473 end
n la 566 0.07177276185645448 contain
n nd 565 0.07164595485670809 contain
n es 549 0.06961704286076592 contain
n me 548 0.06949023586101953 contain
n ng 533 0.06758813086482374 contain
n et 512 0.06492518387014963 contain
n al 508 0.06441795587116407 contain
n nt 490 0.06213542987572914 contain
n ri 480 0.060867359878265276 contain
n ter 477 0.06048693887902613 contain
n ha 470 0.05959928988080144 contain
n is 461 0.05845802688308395 contain
n fe 460 0.05833121988333756 contain
n che 460 0.05833121988333756 contain
n it 451 0.05718995688562008 contain
n um 451 0.05718995688562008 contain
n un 451 0.05718995688562008 contain
n us 437 0.05541465888917068 contain
n at 434 0.05503423788993152 contain
n or 433 0.054907430890185134 contain
n ar 432 0.05478062389043875 contain
n ne 426 0.05401978189196044 contain
n hen 420 0.05325893989348212 contain
n ze 393 0.0498351509003297 contain
n ste 393 0.0498351509003297 contain
n il 383 0.048567080902865836 contain
n li 382 0.04844027390311945 contain
n ro 379 0.0480598529038803 contain
n tr 379 0.0480598529038803 contain
n ic 378 0.04793304590413392 contain
n we 372 0.0471722039056556 contain
n ns 362 0.04590413390819173 contain
n as 359 0.045523712908952574 contain
n ti 355 0.04501648490996703 contain
n on 351 0.04450925691098149 contain
n hen 344 0.04362160791275679 end
n eb 342 0.04336799391326402 contain
n ke 340 0.04311437991377124 contain
n ve 339 0.042987572914024864 contain
n ta 331 0.041973116916053765 contain
n sp 329 0.04171950291656098 contain
n chen 322 0.04083185391833629 contain
n am 318 0.04032462591935075 contain
n ier 318 0.04032462591935075 contain
n ht 316 0.04007101191985798 contain
n sc 315 0.03994420492011159 begin
n ich 313 0.03969059092061882 contain
n sch 312 0.03956378392087243 begin
n cht 310 0.03931016992137966 contain
n um 308 0.039056555921886886 end
n ma 307 0.0389297489221405 contain
n ren 302 0.038295713923408574 contain
n ab 294 0.03728125792543748 contain
n mi 292 0.037027643925944716 contain
n ver 292 0.037027643925944716 contain
n rn 289 0.03664722292670555 contain
n eh 283 0.03588638092822724 contain
n hr 283 0.03588638092822724 contain
n rt 283 0.03588638092822724 contain
n ts 283 0.03588638092822724 contain
n ein 283 0.03588638092822724 contain
n em 280 0.035505959928988085 contain
n na 280 0.035505959928988085 contain
n el 277 0.03512553892974892 end
n ac 277 0.03512553892974892 contain
n aus 276 0.03499873193000254 contain
n ko 275 0.03487192493025615 contain
n gs 274 0.034745117930509765 contain
n ge 271 0.03436469693127061 begin
n ten 269 0.034111082931777836 contain
n ent 267 0.03385746893228506 contain
n ck 266 0.03373066193253866 contain
n chen 259 0.03284301293431397 end
n ni 259 0.03284301293431397 contain
n ec 257 0.032589398934821205 contain
n si 257 0.032589398934821205 contain
n ch 253 0.03208217093583566 end
n ol 253 0.03208217093583566 contain
n rs 250 0.0317017499365965 contain
n ld 247 0.03132132893735734 contain
n and 244 0.03094090793811818 contain
n ka 242 0.03068729393862541 contain
n bi 240 0.030433679939132638 contain
n to 239 0.03030687293938625 contain
n gen 239 0.03030687293938625 contain
n fa 237 0.030053258939893482 contain
n pe 235 0.029799644940400712 contain
n eu 234 0.029672837940654326 contain
n sen 234 0.029672837940654326 contain
n men 232 0.029419223941161563 contain
n ot 228 0.028911995942176007 contain
n wa 227 0.028785188942429624 contain
n st 225 0.02853157494293685 begin
n ngs 224 0.028404767943190464 contain
n ru 223 0.02827796094344408 contain
n ren 222 0.028151153943697688 end
n ag 221 0.028024346943951305 contain
n au 221 0.028024346943951305 begin
n hl 218 0.02764392594471215 contain
n tu 217 0.02751711894496576 contain
n pi 216 0.027390311945219376 contain
n ach 216 0.027390311945219376 contain
n ba 214 0.0271366979457266 contain
n ut 213 0.02700989094598022 contain
n ung 212 0.02688308394623384 contain
n hi 209 0.026502662946994673 contain
n ern 209 0.026502662946994673 contain
n nde 209 0.026502662946994673 contain
n rk 205 0.02599543494800913 contain
n tz 204 0.025868627948262744 contain
n di 203 0.025741820948516358 contain
n pr 200 0.0253613999492772 contain
n uc 200 0.0253613999492772 contain
n ben 199 0.02523459294953081 contain
n lt 197 0.024980978950038042 contain
n nk 197 0.024980978950038042 contain
n ve 197 0.024980978950038042 begin
n uf 195 0.02472736495054527 contain
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n im 193 0.0244737509510525 contain
n pa 192 0.02434694395130612 contain
n ah 189 0.02396652295206696 contain
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n ver 188 0.023839715952320567 begin
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n om 185 0.02345929495308141 contain
n sa 185 0.02345929495308141 contain
n br 184 0.023332487953335024 contain
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n hau 183 0.02320568095358864 contain
n zi 182 0.02307887395384225 contain
n ad 178 0.02257164595485671 contain
n ter 176 0.022318031955363936 end
n rei 176 0.022318031955363936 contain
n fl 175 0.02219122495561755 contain
n bl 174 0.022064417955871163 contain
n ld 172 0.0218108039563784 end
n ers 172 0.0218108039563784 contain
n tel 172 0.0218108039563784 contain
n ken 171 0.02168399695663201 contain
n uch 170 0.02155718995688562 contain
n icht 170 0.02155718995688562 contain
n in 168 0.021303575957392847 end
n ed 167 0.02117676895764646 contain
n ho 166 0.02104996195790008 contain
n ges 165 0.02092315495815369 contain
n ur 162 0.02054273395891453 contain
n den 162 0.02054273395891453 contain
n ing 162 0.02054273395891453 contain
n ab 161 0.020415926959168145 begin
n ten 160 0.02028911995942176 end
n id 159 0.020162312959675376 contain
n ul 159 0.020162312959675376 contain
n lei 159 0.020162312959675376 contain
n ht 158 0.02003550595992899 end
n erk 158 0.02003550595992899 contain
n cht 157 0.019908698960182603 end
n wi 157 0.019908698960182603 contain
n al 156 0.019781891960436216 end
n gr 153 0.019401470961197056 contain
n rg 153 0.019401470961197056 contain
n ug 153 0.019401470961197056 contain
n ig 151 0.019147856961704287 contain
n kr 149 0.018894242962211514 contain
n ster 149 0.018894242962211514 contain
n sen 148 0.018767435962465127 end
n be 148 0.018767435962465127 begin
n ben 146 0.018513821962972358 end
n ech 146 0.018513821962972358 contain
n ha 146 0.018513821962972358 begin
n gen 145 0.018387014963225968 end
n ei 145 0.018387014963225968 begin
n os 144 0.018260207963479585 contain
n gel 144 0.018260207963479585 contain
n zen 144 0.018260207963479585 contain
n ue 143 0.0181334009637332 contain
n sche 143 0.0181334009637332 contain
n est 142 0.018006593963986812 contain
n wer 142 0.018006593963986812 contain
n rn 141 0.017879786964240425 end
n ka 141 0.017879786964240425 begin
n fi 140 0.017752979964494042 contain
n iu 140 0.017752979964494042 contain
n der 140 0.017752979964494042 contain
n ens 140 0.017752979964494042 contain
n ium 139 0.017626172964747656 contain
n mo 138 0.01749936596500127 contain
n auf 138 0.01749936596500127 contain
n chl 138 0.01749936596500127 contain
n bo 137 0.017372558965254883 contain
n chi 137 0.017372558965254883 contain
n nge 137 0.017372558965254883 contain
n pie 136 0.017245751965508496 contain
n pf 135 0.01711894496576211 contain
n hn 134 0.016992137966015727 contain
n ir 134 0.016992137966015727 contain
n ser 134 0.016992137966015727 contain
n us 133 0.016865330966269337 end
n bu 133 0.016865330966269337 contain
n kt 133 0.016865330966269337 contain
n rd 133 0.016865330966269337 contain
n eis 133 0.016865330966269337 contain
n eri 133 0.016865330966269337 contain
n erb 132 0.016738523966522954 contain
n isc 132 0.016738523966522954 contain
n len 132 0.016738523966522954 contain
n nte 132 0.016738523966522954 contain
n isch 132 0.016738523966522954 contain
n kl 130 0.01648490996703018 contain
n rm 129 0.016358102967283794 contain
n age 129 0.016358102967283794 contain
n nd 128 0.01623129596753741 end
n cke 128 0.01623129596753741 contain
n fen 128 0.01623129596753741 contain
n haus 127 0.016104488967791018 contain
n re 127 0.016104488967791018 begin
n ium 126 0.015977681968044638 end
n ef 125 0.01585087496829825 contain
n eit 125 0.01585087496829825 contain
n da 123 0.015597260968805478 contain
n su 123 0.015597260968805478 contain
n geb 123 0.015597260968805478 contain
n sta 123 0.015597260968805478 contain
n ew 122 0.01547045396905909 contain
n hu 122 0.01547045396905909 contain
n nz 122 0.01547045396905909 contain
n hei 121 0.015343646969312704 contain
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n et 119 0.015090032969819934 end
n so 119 0.015090032969819934 contain
n fer 119 0.015090032969819934 contain
n her 119 0.015090032969819934 contain
n mit 119 0.015090032969819934 contain
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n schi 119 0.015090032969819934 contain
n an 119 0.015090032969819934 begin
n ki 118 0.014963225970073548 contain
n hw 117 0.014836418970327163 contain
n eld 117 0.014836418970327163 contain
n lie 117 0.014836418970327163 contain
n ko 117 0.014836418970327163 begin
n ken 116 0.014709611970580775 end
n oc 116 0.014709611970580775 contain
n po 116 0.014709611970580775 contain
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n em 115 0.01458280497083439 end
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n no 115 0.01458280497083439 contain
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n mp 114 0.014455997971088003 contain
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n tra 114 0.014455997971088003 contain
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n gl 112 0.014202383971595232 contain
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n if 110 0.013948769972102459 contain
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n fo 109 0.013821962972356074 contain
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n af 106 0.013441541973116916 contain
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n icht 104 0.013187927973624143 end
n ft 104 0.013187927973624143 contain
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n ern 103 0.013061120973877759 end
n men 103 0.013061120973877759 end
n ik 103 0.013061120973877759 contain
n rf 103 0.013061120973877759 contain
n rz 103 0.013061120973877759 contain
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n eck 102 0.012934313974131372 contain
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n alt 101 0.012807506974384986 contain
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n rech 98 0.012427085975145828 contain
n oh 97 0.012300278975399441 contain
n stem 97 0.012300278975399441 contain
n ku 96 0.012173471975653057 contain
n spie 96 0.012173471975653057 contain
n hm 95 0.01204666497590667 contain
n erg 95 0.01204666497590667 contain
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Here we hope to find our rules.

The sequence Value matched can appear at the end (end) or anywhere (contain) in the word.

In the previous textbook heuristics table we had a number of rules per gender:

  • Masculine: 40 rules
  • Feminine: 15 rules
  • Neutral: 13 rules
  • Total (40 + 15 + 13) = 68 rules

A new set of rules

In our quest to reduce cognitive load we are looking for better rules than the ones you have seen. They are pretty accurate. Can we reduce the number of rules to memorize? Armed with the big table of characters groups we can start looking.

First try: best 20 rules based on ending

Let’s try to find the best rules from all the permutations possible. As a first exploration we will focus on ending in rules and try with a maximum number of 20 rules.

Masculine Feminine Neutral
  -ung -hen
  -it -chen
  -eit -ren
  -te  
  -ion  
  -se  
  -ie  
  -le  
  -tion  
  -rin  
  -rung  
  -ge  
  -erin  
  -keit  
  -ne  
  -de  
  -tung  

20 best ending in rules. If it ends in the value we classify it as in that gender.

No masculine rule made it to the top 20.

Results best 20

image

It is pretty accurate

Textbook % match Ours % match Difference match
0.27 0.26 0.02
Textbook f % correct Textbook f % correct.1 Difference f % correct
0.64 0.64 -0.28
Textbook m % correct Textbook m % correct.1 Difference m % correct
0.23 0.23 0.23
Textbook n % correct Textbook n % correct.1 Difference n % correct
0.14 0.14 0.05

2% improvement over the textbook. We can also see how each gender m, f and n perform individually with textbook rules vs ours.

Both the graph and the numbers give us the same insights. Our first guess, with 20 rules already performs 2% better than the baseline!

In search of even better rules

Until now we checked only 20 rules. Why 20? since the textbook had (as seen before):

  • Masculine: 40 rules
  • Feminine: 15 rules
  • Neutral: 13 rules
  • Total (40 + 15 + 13) = 68 rules

20 was just a guess to explore the data. What is the optimal number of ending in rules that gives as the greatest accuracy? Let’s find out.

Best rules based on begins, contains or ending

Until now we checked only 20 rules per gender, where the rule is “word ends in” -ung, -er, etc. mimicking the rules from the textbook. Probably ending in is easier to remember, but we do not need to limit ourselves to this rule. It might be that contains (anywhere in the word) produces an easier to remember rule with the same or better accuracy.

We will run the best rules from 1 rule to 68 (the textbook number of rules) and see how accurate they are.

Results from 1 to 68 rules

Textbook % match Ours % match Difference match Rule # Match type
0.27 0.45 -0.17 68 end
0.27 0.45 -0.17 67 end
0.27 0.44 -0.17 66 end
0.27 0.44 -0.17 65 end
0.27 0.44 -0.17 64 end
0.27 0.44 -0.16 63 end
0.27 0.43 -0.16 62 end
0.27 0.43 -0.15 60 end
0.27 0.43 -0.15 58 end
0.27 0.43 -0.15 59 end
0.27 0.43 -0.16 61 end
0.27 0.42 -0.14 55 end
0.27 0.42 -0.15 56 end
0.27 0.42 -0.15 57 end
0.27 0.41 -0.14 54 end
0.27 0.41 -0.13 53 end
0.27 0.4 -0.13 51 end
0.27 0.4 -0.12 49 end
0.27 0.4 -0.13 50 end
0.27 0.4 -0.13 52 end
0.27 0.39 -0.12 47 end
0.27 0.39 -0.12 46 end
0.27 0.39 -0.12 48 end
0.27 0.38 -0.11 45 end
0.27 0.38 -0.1 44 end
0.27 0.37 -0.1 43 end
0.27 0.37 -0.1 42 end
0.27 0.36 -0.09 40 end
0.27 0.36 -0.09 41 end
0.27 0.35 -0.08 38 end
0.27 0.35 -0.08 39 end
0.27 0.35 -0.07 37 end
0.27 0.34 -0.07 36 end
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0.27 0.33 -0.06 34 end
0.27 0.33 -0.06 33 end
0.27 0.33 -0.05 32 end
0.27 0.32 -0.05 31 end
0.27 0.31 -0.04 30 end
0.27 0.31 -0.04 29 end
0.27 0.3 -0.03 28 end
0.27 0.29 -0.02 26 end
0.27 0.29 -0.01 25 end
0.27 0.29 -0.02 27 end
0.27 0.28 -0.01 23 end
0.27 0.28 -0.01 24 end
0.27 0.27 0.0 22 end
0.27 0.26 0.02 65 contain
0.27 0.26 0.01 66 contain
0.27 0.26 0.01 67 contain
0.27 0.26 0.01 68 contain
0.27 0.26 0.01 21 end
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0.27 0.26 0.02 19 end
0.27 0.25 0.02 64 contain
0.27 0.25 0.02 62 contain
0.27 0.25 0.02 63 contain
0.27 0.25 0.03 60 contain
0.27 0.25 0.03 61 contain
0.27 0.24 0.03 59 contain
0.27 0.24 0.03 18 end
0.27 0.24 0.04 58 contain
0.27 0.23 0.05 16 end
0.27 0.23 0.04 57 contain
0.27 0.23 0.04 17 end
0.27 0.23 0.05 55 contain
0.27 0.23 0.04 56 contain
0.27 0.23 0.05 14 end
0.27 0.23 0.05 15 end
0.27 0.22 0.05 54 contain
0.27 0.22 0.05 52 contain
0.27 0.22 0.06 51 contain
0.27 0.22 0.05 53 contain
0.27 0.21 0.07 47 contain
0.27 0.21 0.06 13 end
0.27 0.21 0.06 49 contain
0.27 0.21 0.06 50 contain
0.27 0.21 0.06 12 end
0.27 0.21 0.06 48 contain
0.27 0.21 0.06 11 end
0.27 0.2 0.07 45 contain
0.27 0.2 0.07 10 end
0.27 0.2 0.07 46 contain
0.27 0.2 0.07 44 contain
0.27 0.2 0.07 9 end
0.27 0.19 0.08 42 contain
0.27 0.19 0.08 41 contain
0.27 0.19 0.08 43 contain
0.27 0.18 0.09 40 contain
0.27 0.18 0.09 8 end
0.27 0.18 0.1 39 contain
0.27 0.17 0.11 36 contain
0.27 0.17 0.1 37 contain
0.27 0.17 0.1 38 contain
0.27 0.17 0.11 7 end
0.27 0.17 0.11 35 contain
0.27 0.16 0.11 34 contain
0.27 0.16 0.11 33 contain
0.27 0.16 0.11 32 contain
0.27 0.16 0.11 31 contain
0.27 0.16 0.11 30 contain
0.27 0.16 0.11 29 contain
0.27 0.16 0.11 28 contain
0.27 0.16 0.11 27 contain
0.27 0.15 0.12 6 end
0.27 0.15 0.12 26 contain
0.27 0.15 0.12 25 contain
0.27 0.15 0.12 24 contain
0.27 0.15 0.12 23 contain
0.27 0.15 0.13 22 contain
0.27 0.14 0.13 21 contain
0.27 0.13 0.14 20 contain
0.27 0.13 0.14 5 end
0.27 0.13 0.15 19 contain
0.27 0.12 0.15 4 end
0.27 0.12 0.16 18 contain
0.27 0.11 0.17 15 contain
0.27 0.11 0.17 16 contain
0.27 0.11 0.17 17 contain
0.27 0.1 0.18 2 end
0.27 0.1 0.18 3 end
0.27 0.1 0.18 14 contain
0.27 0.08 0.19 10 contain
0.27 0.08 0.19 11 contain
0.27 0.08 0.19 9 contain
0.27 0.08 0.19 13 contain
0.27 0.08 0.19 12 contain
0.27 0.07 0.2 6 contain
0.27 0.07 0.2 7 contain
0.27 0.07 0.2 8 contain
0.27 0.07 0.2 1 end
0.27 0.05 0.22 5 contain
0.27 0.04 0.23 4 contain
0.27 0.02 0.25 1 contain
0.27 0.02 0.25 3 contain
0.27 0.02 0.25 2 contain

We take the best rules from 1 rule to 68 rules and measure their accuracy vs the baseline (“Ours % match” vs “Textbook % match”)

Below we can see this data in a more intuitive way:

image

The letters at the end of a word (ending) predict the gender better than in other positions (contain)

We can see clearly:

  • Textbook rules match 27% of the time.
  • Ending rules are more accurate that contain rules.
  • With just 22 rules we can achieve the same accuracy as with textbook’s 68 rules.

Or, alternatively:

  • with 68 rules (same as textbook) we can increase the accuracy from 27%to 45%.

The new rules

In the previous section we calculated the different tradeoffs between accuracy and number of rules to remember. How many rules are you willing to memorize?

If you only remember one rule

You cannot be bothered, tempus fugit. If you can only learn one rule, learn this one.

Masculine Feminine Neutral
  -ung  

You will be right 7% of the time.

To be 20% accurate: 10 rules

Even a broken clock is right twice a day. If you are happy with a set of rules correct 20% of the time use these 10 rules:

Masculine Feminine Neutral
  -ung -hen
  -it  
  -eit  
  -te  
  -ion  
  -se  
  -ie  
  -le  
  -tion  

To guess the same amount of words as with the textbook rules: 27%. You will have to memorize a bit less6: instead of the 68 rules before, only the 22 below:

Masculine Feminine Neutral
-tz -ung -hen
-ck -it -chen
  -eit -ren
  -te  
  -ion  
  -se  
  -ie  
  -le  
  -tion  
  -rin  
  -rung  
  -ge  
  -erin  
  -keit  
  -ne  
  -de  
  -tung  

Maximum accuracy: 68 rules

Only the best is enough. Perfection is not the enemy of the good for you, but the only good. Then, study these 68 rules — same number as before, from our textbook — for a correct guess 45% of the time, versus a now pale 27%.

Masculine Feminine Neutral
-tz -ung -hen
-ck -it -chen
-st -eit -ren
-ler -te -ld
-ag -ion -al
-or -se -sen
-sch -ie -ben
-mus -le -rn
-kt -tion -ium
-her -rin -et
-ang -rung -ken
-ug -ge -em
-rt -erin -aus
-atz -keit -haus
-aum -ne -icht
-pf -de -ern
-ger -tung -men
  -ft -eln
  -lung -eld
  -heit  
  -he  
  -be  
  -aft  
  -ik  
  -che  
  -pe  
  -age  
  -dung  
  -ke  
  -ei  
  -nde  
  -haft  

Coda

Thanks for reading until the very end! If you are learning german, I hope the new 22 rules make it slightly easier.

If you know of another set of rules to guess the gender of a noun in german I would be happy to hear from them. Viel Spaß! :)

Thanks for reading


Footnotes

  1. https://en.wikipedia.org/wiki/Heuristic 

  2. You can find the data on the github repository 

  3. Will somebody think of our friend cognitive load. 

  4. After looking in the dusty Studio D A1: Deutsch als Fremdsprache and Studio D A2: Deutsch als Fremdsprache I could not find gender heuristics explicitely in the text (the teacher must have explained them outside of the book) I found a good list in Gramática funcional del alemán p.215 ( ISBN: 84-7960-095-0) 

  5. For instance, words containing en match in 20-36% all three genders.

    Gender matched Value matched Num matched Percent matched Match type
    n en 2819 0.357468 contain
    m en 2369 0.208191 contain
    f en 3178 0.221201 contain

    Not very useful rules: they match everything. The percentage of the same gender matched by the rule. If the rule applies to neutral, it matches n% of the total of words with neutral gender. The percentage ranges from 0.0 to 1.0 — i.e. 0.03 represents 3 % —

  6. El saber no ocupa lugar, knowledge takes no room/space. A nice spanish saying, maybe from somebody who did not often have to cram for an exam.