Analyzing the Anglo-Saxonicity of the Baby BNC


This is a followup to an earlier post about using Python to measure the “Anglo-Saxonicity” of a text. I’ve used my code to analyze the Baby version of the British National Corpus, and I’ve found some interesting results.

How to Measure Anglo-Saxonicity – With a Ruler or Yardstick?


Thanks to a suggestion from Ben Sizer, I decided to analyze the British National Corpus. I started with the ‘baby’ corpus which, as you might imagine, is smaller than the full corpus.

It’s described as a “100 million word snapshot of British English at the end of the twentieth century“. It categorizes text samples into four groups: academic, conversations, fiction, and news. Below are stack plots showing the percentage of Anglo-Saxon, non-Anglo-Saxon, and unknown words for each document in each of the four groups. The Y axis shows the percentage of words in each category. The numbers along the X axis identify individual documents within the group.

I’ve deliberately given the charts non-specific names of Group A, B, C, and D so that we can play a game. :-)

Before we get to the game, here’s the averages for each group in table form. (The numbers might not add exactly to 100% due to rounding.)

Anglo-Saxon (%) Non-Anglo-Saxon (%) Unknown (%)
Group A 67.0 17.7 15.3
Group B 56.1 25.8 18.1
Group C 72.9 13.2 13.9
Group D 58.6 22.0 19.3

Keep in mind that “unknown” words represent shortcomings in my database more than anything else.

The Game

The Baby BNC is organized into groups of academic, conversations, fiction, and news. Groups A, B, C, and D each represent one of those groups. Which do you think is which?

Click below to reveal the answer to the game and a discussion of the results.


Anglo-Saxon (%) Non-Anglo-Saxon (%) Unknown (%)
A = Fiction 67.0 17.7 15.3
B = Academic 56.1 25.8 18.1
C = Conversations 72.9 13.2 13.9
D = News 58.6 22.0 19.3


With the hubris that only 20/20 hindsight can provide, I’ll say that I don’t find these numbers terribly surprising. Conversations have the highest proportion of Anglo-Saxon (72.9%) and the lowest of non-Anglo-Saxon (13.2%). Conversations are apt to use common words, and the 100 most common words in English are about 95% Anglo-Saxon. The relatively fast pace of conversation doesn’t encourage speakers to pause to search for those uncommon words lest they bore their listener or lose their chance to speak. I think the key here is not the fact that conversations are spoken, but that they’re impromptu. (Impromptu if you’re feeling French, off-the-cuff if you’re more Middle-English-y, or extemporaneous if you want to go full bore Latin.)

Academic writing is on the opposite end of the statistics, with the lowest portion of Anglo-Saxon words (56.1%) and the highest non-Anglo-Saxon (25.8%). Academic writing tends to be more ambitious and precise. Stylistically, it doesn’t shy away from more esoteric words because its audience is, by definition, well-educated. It doesn’t need to stick to the common core of English to get its point across. In addition, those who shaped academia were the educated members of society, and for many centuries education was tied to the church or limited to the gentry, and both spoke a lot of Latin and French. That has probably influenced even the modern day culture of academic writing.

Two of the academic samples managed to use fewer than half Anglo-Saxon words. They are a sample from Colliding Plane Waves in General Relativity (a subject Anglo-Saxons spent little time discussing, I’ll wager) and a sample from The Lancet, the British medical journal (49% and 47% Anglo-Saxon, respectively). It’s worth noting that these samples also displayed highest and 5th highest percentage of words of unknown etymology (26% and 21%, respectively) of the 30 samples in this category. A higher proportion of unknowns depresses the results in the other two categories.

Fiction rests in the middle of this small group of 4 categories, and I’m a little surprised that the percentage of Anglo-Saxon is as high as it is. I feel like fiction lends itself to the kind of description that tends to use more non-Anglo-Saxon words, but in this sample it’s not all that different from conversation.

News stands out for having barely more Anglo-Saxon words than academic writing, and also the highest percentage of words of unknown etymological origin. The news samples are drawn principally from The Independent, The Guardian, The Daily Telegraph, The Belfast Telegraph, The Liverpool Daily Post and Echo, The Northern Echo, and The Scotsman. It would be interesting to analyze each of these groups independently to see if they differ significantly.


My hypothesis that conversations have a high percentage of Anglo-Saxon words because they’re off-the-cuff rather than because they’re spoken is something I can challenge with another experiment. Speeches are also spoken, but they’re often written in advance, without the pressure of immediacy, so the author would have time to reach for a thesaurus. I predict speeches will have an Anglo-Saxon/non-Anglo-Saxon profile closer to that of fiction than of either of the extremes in this data. It might vary dramatically based on speaker and audience, so I’ll have to choose a broad sample to smooth out biases.

I would also like to work with the American National Corpus.

Stay tuned, and let me know in the comments if you have observations or suggestions!





How to Measure Anglo-Saxonicity – With a Ruler or Yardstick?

Summary (Nutshell)

This is a first look at a work in progress. I’m using Python to study text from an etymological perspective. Specifically, I’m measuring how many words in a given English language text have Anglo-Saxon origin. Many people (including myself) think that Anglo-Saxon words convey a different sense than their counterparts of French/Latin origin. To demonstrate the point in a small way, I’ve included a Latin and Anglo-Saxon version of each heading in this blog post.

Background (Milieu)

English is a Germanic language with Scandinavian influence, with a big layer of Old French poured on top. That Old French (Anglo Norman French, to be specific) was principally derived from Latin, so English is a hybrid between two major Indo-European language groups. Those mongrel origins are a big part of why English is messy and rich.

French was introduced to English as the language of conquerers and nobility. French was also the language of some European royalty in the 18th and 19th century, further adding to its reputation as a language associated with high status. Even today, English words with French origins often have higher cultural status than their counterparts with Anglo-Saxon origins (think cuisine versus cooking, illumination versus light, create versus make, and escargot versus snail). By contrast, the Anglo-Saxon words are often considered more visceral (think sea versus ocean, sweat versus perspire, and free versus emancipated — more on that last pair in a moment).

For instance, when taunting someone, you reach for blunt Anglo-Saxon words. “Your mother was a hamster, and your father smelled of elderberries!” is 100% Anglo-Saxon, except for “elderberries” which was coined in Middle English from “elder” and “berry”, both of Anglo-Saxon origin.

A still of the French taunting King Arthur from Monty Python and the Holy Grail
William of Normandy in 1067, addressing his English subjects.

Legal documents and government issuances, on the other hand, tend to include more words of Latin/French origin. It’s no coincidence that the Latin/French words “Emancipation Proclamation” describe a legal act, but if you want to stir the heart about emancipation, you say something like “Free at last!”(1)  which is all Anglo-Saxon.

Others have written more eloquently than I about how word origin influences tone (Annalisa Quinn at NPRGemma Varnom, and M. Birch, to suggest a few), so I won’t belabor the point more than I already have. But I wanted to talk about how it inspired the project I’ve been working on.

The Project (The Work)

I should preface this by saying that I Am Not A Linguist, and I don’t even play one on TV.

I thought it would be interesting to perform lexicographical analysis of text from an etymological perspective. My etymological categorization is necessarily simple. When I look at a text, I put each word into one of three etymological categories: Anglo-Saxon, non-Anglo-Saxon, or unknown. From this rough grouping I generate statistics that allow me to compare one text to another.

For instance, does one author consistently use more Anglo-Saxon words than other authors? Does an author’s usage of Anglo-Saxon words change from one work to another? Also of interest to me is the etymology of words as the book progresses from front to back. Do the relative frequencies of etymologies change as the book progresses towards its exciting conclusion? For authors writing in English as a second language, is their word selection influenced by their first language?

All of the questions above can be explored with the tool I’ve written. It’s easier to show the tool’s output than describe it, so here’s an analysis of Lewis Carroll’s 1865 work “Alice’s Adventures in Wonderland”.

The graph below shows the relative frequency of the three etymological categories as the book progresses from beginning to end.

A graphical representation of how the etymological ration of Alice in Wonderland changes as one progresses through the book

This graph shows the relative frequency of the three etymological categories as counting statistics for various part-of-speech categories.

A graphical representation of the counts of words by parts of speech and etymology in Alice in Wonderland

The table below is a more detailed version of the chart immediately above. Some percentages may not add up to 100% due to rounding.

Total %age of All Words Anglo-Saxon non-Anglo-Saxon Unknown
All Words 26624 100% 18233 (68%) 3812 (14%) 4579 (17%)
Unique 3528 13% 1354 (38%) 899 (25%) 1275 (36%)
Nouns 8522 32% 4521 (53%) 2354 (27%) 1647 (19%)
Verbs 5479 20% 2994 (54%) 565 (10%) 1920 (35%)
Adjectives 1639 6% 896 (54%) 375 (22%) 368 (22%)
Adverbs 1974 7% 1348 (68%) 420 (21%) 206 (10%)
Other 9010 33% 8474 (94%) 98 (1%) 438 (4%)

Observations (What I See)

There’s some minor observations to be made here, but the strength of this tool will be in comparative analysis. It’s hard to draw conclusions from one analysis before I have an idea of what’s typical.

For instance, at first glance, the ratio of Anglo-Saxon to non-Anglo-Saxon words looks dramatic, but this says more about English than it does about Carroll. The most common words in English are overwhelmingly Anglo-Saxon in origin. (2)  For the small sample size of works I’ve processed so far (just 8 in total), I can see that it’s common for roughly three quarters of the words to be Anglo-Saxon. Alice in Wonderland isn’t an outlier by that standard.

We can also see that the frequency of Anglo-Saxon words decreases slightly throughout the book. This is the kind of trend that I find interesting, but in this case it’s due to an increase in the number of words of unknown etymology. Sometimes a word’s etymology is truly unknown. More often, though, the etymology is classified as unknown for other reasons. Most likely, it’s simply not in my etymological database (which isn’t very complete yet). Also, the word could be a proper noun, an invented word (like “woodshadows” from James Joyce’s Ulysses), or a word for which the etymology is ambiguous. An example of this last category is “bank” which is Anglo-Saxon in origin when referring to the side of a river, but French/Italian in origin when referring to a place that handles money.

At present, the quantity of words classified as “unknown” is too large for my tastes, and I plan to reduce it by improving both my database and the tool.

Verbs are overrepresented in the “unknown” category. My guess is that this is an artifact of my stemmer having difficulty stemming verbs. (I’m currently using the Snowball Stemmer from NLTK.)

As you can see, at this point it’s easier to draw conclusions about the representation of the data than it is about the data themselves. That leads me to the next (and final) topic in this post.

Future (What’s to Come)

As I said in the introduction, this is an early look at a work in progress. Here’s some of the things I’d like to add –

  • Better etymological data
  • Large scale comparisons of text to look for trends (across authors, genres, etc.)
  • More numeric (rather than visual) descriptions of the data to facilitate automated comparison. One idea is to add the mean and standard deviation of the percentage of Anglo-Saxon words.
  • Open sourcing

If you have any suggestions on how to use this tool or make it more interesting, I’d love to hear them in the comments below. I moderate all comments to filter spam which is yet another Viking influence on England.


Like English itself, “Endnote” is an etymological hybrid. “End” is of Anglo-Saxon origin, while  “note” comes from Old French/Latin.

1. Martin Luther King, Jr. isn’t the only person to have said “Free at last!”, but his use of it is perhaps the most famous. His “I Have a Dream” speech makes brilliant use of etymological contrasts. Many of his memorable phrases in that speech (“I have a dream today”, “Let freedom ring”, “Free at last”) are Anglo-Saxon.

2. In 2014 I pulled from Wikipedia a list of the 100 most common English words. At the time, it contained just four non-Anglo-Saxon words. They were “just” (ME < Latin), “people” (ME < Anglo-French < Latin), “use” (ME < Old French, replaced OE brucan, cognate w/modern Swedish bruk-), and “because” (ME < Fr ‘par cause’). There are lots of ways to count the 100 most common words, and doubtless the list would have been different in Carroll’s day. But my guess is that the presence of Anglo-Saxon hasn’t changed dramatically from that 96% regardless of when and how one counts.