Methods & Notes
I compared two prominent vocabulary lists for English learners: the General Service List (GSL, 1953; 2,284 words) and the New General Service List (NGSL 1.2, 2023; 2,809 words). I labeled words appearing on both lists as “remained” (1,656), words only on the 1953 list as “removed” (628), and words only on the 2023 list as “added” (1,153).
The GSL words came from the
Simple English Wiktionary GSL
. The NGSL words came from the
official NGSL 1.2 file
(“alphabetized and lemmatized for research”).
The NGSL uses lemmas (one entry per word family); the GSL sometimes lists inflected forms as separate headwords. These lists track word forms deemed worth teaching based on frequency and usefulness, not abstract concepts. For example, the word
being
is on the GSL as its own headword and was not included in the NGSL, But this doesn’t mean the concept of existence left the language. In the NGSL it falls under
be
, which is on both lists.
Why treat the lists as a portrait of everyday English?
Both lists were built for teaching, but external research suggests each covers a large share of everyday language use. About 84% of general English for the GSL and about 90% for the NGSL, depending on the text and how words are counted. I did not re-run those corpus analyses myself. I take the published materials as given and rely on coverage figures from the list authors and from follow-up studies (including an independent check on American English by Stoeckel, 2019 – see footnotes). That’s why the differences between the lists felt worth examining as more than a curriculum update, with the caveat that neither list is a neutral census of culture.
Where can I find the data?
All tagged words: remained, removed, and added, with semantic tags, concreteness ratings, and part-of-speech labels are in
this public spreadsheet
. You can also browse the word lists in the word-list panel on the right side of this page.
How did I sort words by meaning?
Each word was tagged with the UCREL Semantic Analysis System (USAS), using the 21 top-level categories. USAS also assigns much finer
sub-categories
(there are hundreds), but I stayed at the top level so the charts could show broad shifts without splitting the words into overly granular bins.
I chose not to correct mislabels. For example,
hammer, nail,
and
wax
are all tagged “General and Abstract Terms”, but in USAS’s finer tags, they read as actions (“to hammer,” “to nail,” “to wax”), not objects. Out of context, many of these words go multiple ways, and it didn’t feel right to override that case by case; USAS is an established linguistic framework, and swapping in my own judgment would mix two different standards.
For the second chart, I grouped USAS’s 21 categories into five “scope” domains (self, local, institutional, social, abstract). That grouping is my editorial choice, not part of USAS. It came from noticing a spatial quality to the trends seen across the 21 categories.
How did I measure concreteness?
Concreteness ratings come from
Brysbaert et al. (2014).
I used these as-is. Six words weren’t in the database and were left out of the concreteness charts:
as, dialog, english, gaiety, madden,
and
old-fashioned.
How did I tag parts of speech?
Parts of speech were tagged with NLTK, simplified to five categories. Here I did intervene, but only when a word was clearly mislabeled (132 words, 3.8% of the list; mostly adjectives mislabeled as nouns). When a word can act as more than one part of speech depending on context, I left the tag as-is and deferred to NLTK as the established framework, using the primary, most common label. Here I also used an LLM strictly to help flag potential errors in NLTK’s output.
What are the limitations?
Both lists rank words by frequency, then apply learner-focused curation. Michael West’s 1953 list especially reflects period pedagogy. He favored general-purpose vocabulary over emotional or highly specific words, not just whatever appeared most often (
Therova, 2020
, summarizing West, 1953, pp. ix–x). Some of what looks like “1950s life” may also be how mid-century ESL teaching filtered the language. I still treat the lists as a portrait of everyday English because both cover a large share of running text and speech that goes well beyond the classroom.
Footnotes
On NGSL’s ~90% coverage:
Browne, Culligan & Phillips, NGSL project.
See also:
A New General Service List: The Better Mousetrap We’ve Been Looking for?
, Browne (2014); and
An Examination of the New General Service List
, Stoeckel (2019).
⏎
On GSL’s ~84% coverage:
A general service list of English words with semantic frequencies,
West (1953);
The New General Service List: A core vocabulary for EFL students and teachers
, Cambridge ELT (2018).
⏎
UCREL: Semantic Analysis System (USAS)
.
⏎
Concreteness ratings for 40 thousand generally known English word lemmas
, Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Behavior Research Methods, 46, 904–911.
⏎
Why are pictures easier to recall than words?
Paivio, A., Rogers, T.B. & Smythe, P.C. Psychon Sci 11, 137–138 (1968).
⏎
