How Many Words Do We Know? Practical Estimates of Vocabulary Size Dependent on Word Definition, the Degree of Language Input and the Participant’s Age

It is unavoidable that the estimates from Table 4 are approximations, dependent on the choices made. All we can do, is be transparent about the ways in which we derived the figures and to make our lists publicly available, so that other researchers can adapt them if they feel a need to do so.

A first limitation is the list of 61,800 lemmas we used. Although we are reasonably sure the list contains the vast majority of words people are likely to know, there are ample opportunities to increase the list. As indicated above, the Collins scrabble list could be used to more than double the number of entries. We are fairly confident, however, that such an increase will not change much in the words known by the participants (see also Goulden et al., 1990). The words we are most likely to have missed are regionally used common words and recently introduced words. On the other hand, a recent study by Segbers and Schroeder (2016) in German illustrates the importance of the word list started from. These authors worked with a list of 117,000 lemmas and on the basis of a procedure similar to Goulden et al. (1990) concluded that native German adults know 73,000 lemmas of which 33,000 are monomorphemic (which comes closest to our definition of base lemmas). A comparison of both lists by a German–English researcher would be informative to see why the estimates are larger in German than in English. German has more single-word compounds than English (which partly explains the higher number of lemmas known in German than in English), but this should not affect the number of monomorphemic lemmas known.

A second limitation is that our list does not include meaningful multiword expressions. Expressions such as ‘have to, give in, washing machine, salad spinner, kick the bucket, at all, …’ were excluded from our lemma list. Such sequences are particularly important when the meaning of the expression is not clear from the individual words. Martinez and Schmitt (2012) reported 505 such non-transparent expressions among the 5,000 most frequent words in English, suggesting that the estimates of Table 4 should be increased by 10% to include multiword expressions that cannot be derived from the constituent words.

A third limitation is that our definition of words does not take into account the fact that some words have multiple senses and sometimes even meanings. Words like ‘mind, miss, sack, second’ have several meanings that are unrelated to each other. Goulden et al. (1990) estimated that 15 percent of the entries in Webster’s Third were homographs (word with multiple meanings), of which two thirds did not differ substantially from the previous entry (i.e., the difference was a difference in sense rather than meaning). So, a reasonable estimate would be that 5% of the words are ambiguous in their meaning and our measure has no way to assess whether the participants were familiar with the various meanings. Even worse, our assessment says nearly nothing about how well the participants know the various words. They were only asked to select the words they knew. Estimates of vocabulary size are smaller if more demanding tests are used, as can be seen in Table 3 [e.g., compare the estimate of Zechmeister et al. (1995) to that of D’Anna et al. (1991) and the estimate of Milton and Treffers-Daller (2013) to that of Goulden et al. (1990)]. Indeed, understanding the meaning of words is not a binary, all-or-nothing phenomenon but a continuum involving multiple aspects (Christ, 2011). As such, our estimates should be considered as upper estimates of word knowledge, going for width rather than depth. The estimates also deal with receptive word knowledge (understanding words other people use). As indicated above, productive knowledge is thought to be roughly half of receptive word knowledge.

Finally, our list excludes names (and acronyms). An interesting question is how many names people are likely to know. In principle, these could run in hundreds of thousands (certainly for older people reading a lot, as shown in Table 4). On the basis of our experiences, however, we believe that the number is more likely to be in the tens of thousands or even thousands (depending on the person). For instance, when we probed a large segment of the Dutch-speaking population about their knowledge of fiction authors with a test similar to the vocabulary test described above, we saw that few people know more than 500 author names (out of a total of 15,000 collected from a local library). Hidalgo and colleagues set up a website about world famous people. Thus far, the number includes some 11,000 names. It would be interesting to examine how many of these are effectively known by various people. The smaller estimates agree with the observation of Roberts et al. (2009) that the number of social contacts people have is limited to some 150, although of course people know many more names (of humans and places) than the individuals they have personal relationships with.

Conclusion

Based on an analysis of the literature and a largescale crowdsourcing experiment, we estimate that an average 20-year-old student (the typical participant in psychology experiments) knows 42,000 lemmas and 4,200 multiword expressions, derived from 11,100 word families. This knowledge can be as shallow as knowing that the word exists. Because people learn new words throughout their lives, the numbers are higher for 60-year-olds. The numbers also depend on whether or not the person reads and watches media with verbal content.