Learn English – When was “Uh huh” invented? is “Uh huh” a formal English

The origin seems clear enough: an abbreviation for karaoke television. Whether or not the average person in China understands the origin is probably not relevant as to its popularity; after all, I think relatively few Americans could write out the long form for RSVP.

Several years ago, it was widely reported in anglophone media that some arm of the Chinese government was trying to discourage the use of roman acronyms like NBA (but not Q?), and while I think this is probably an example of press misunderstanding or exaggeration, the reports acknowledge that various acronyms are commonly used in China— indeed that various organs of the Chinese state itself are best known by their English abbreviations, like CCTV and SARFT.

KTV returns zero results from the British National Corpus, and the one entry in the Corpus of Contempotary American English is the name of a television news show. So I turn to the Corpus of Global Web-Based English (GloWbE), which turns up the following results by country:

• Singapore 67
• Hong Kong 35
• The Philippines 20
• Malaysia 6

Almost all of these refer to karaoke or karaoke parlors, so depending on your standard for "English-speaking country," it does seem to be in use in Singapore, Hong Kong, the Philippines, and Malaysia.

There are 8 results for the US, of which 5 come from websites about China and three come from travel blogs about China. The 11 results for the UK, similarly, are about travel in China or entertainment in China. In both cases, KTV is invariably described as the local name for a karaoke parlor.

In contrast, results for karaoke alone show

• GB 867
• US 528
• Singapore 233
• Malaysia 235
• Philippines 198
• Hong Kong 174

In fact, there are even more results for noraebang (the more obscure Korean term for the same entertainment; venues are signed with NRB) in Singapore than for KTV in either the US or UK corpora. The Google Books results for KTV, similarly, refer exclusively to karaoke parlors as they exist in China, Taiwan, or Southeast Asia.

The Yelp category is karaoke (though KTV turns up in some reviews, along the lines of This place reminds me of a KTV/karaoke place in Guangzhou). For Google Maps/Google Local in my area, there are 414 results for karaoke, and only 3 of any kind for KTV, only one of which is for a karaoke joint.

All of this suggests very strongly to me that KTV as a term referring to a karaoke establishment is not a mainstream usage in the US (and does not seem to be in the UK or Canada, either), though expats or tourists might use it, and any businesses catering to them.

These figures are almost always the numbers for the top N words in a corpus. The results can vary considerably depending on what corpus is used and what N is (as you can see in this paper).

"The English language is a lot more French than we thought, here’s why" (by Andreas Simons, on Medium), summarizes one of the sources Wikipedia quotes for the 29% Latin figure:

The latest research was done in 1975 by Joseph M. Williams, where he examined the 10,000 most frequently used words in English, based on a rather small sample size of corporate letters. Here are my issues with his research:

• the research carries a bias towards French and Latin, as companies are more likely to use academic language
• proper names were not removed, possibly diluting the results for an etymological composition
• he used the 10 000 most common words in that corpus of letters, not really “core vocabulary”

Because of these problems, the author found numbers of his own by taking a list of the 5,000 most common English words (which will be about "85% of all words in any English source") and scraping etymology sites (mostly Etymonline) to see what languages were mentioned in the first few words of each word's etymology:

Note that sometimes a word of Latin origin will return “French” using my method. This is because Etymonline always mentions French before Latin if the word entered English through French and the word changed sufficiently from the root. A word such as “origin” (from “origo”) will therefore return French, whereas a word such as “provide” (from “providere — provideo”) will return Latin.

I'm not sure how much I trust the results, but this is the most transparent analysis I found so far — the code used to generate the numbers is linked to in the article. This code can be modified to output words it classifies as Latin. I haven't run the code myself but it looks pretty simple to make these edits. Lines 220-240 in the original Sorter.py are:

    for word in words1:
        print(word)
        origin = scrape_and_interpret(word)
        if origin == "french":
            count_french += 1
            list_french.append(word)
        elif origin == "latin":
            count_latin += 1
            list_latin.append(word)
        elif origin == "old_english":
            count_old_english += 1
            list_old_english.append(word)
        elif origin == "germanic":
            count_germanic += 1
            list_germanic.append(word)
        elif origin == "greek":
            count_greek += 1
            list_greek.append(word)
        elif origin == "other":
            list_other.append(word)
            count_other += 1

Change two lines and get this:

    for word in words1:
        #print(word) # Comment out print statement that prints all words
        origin = scrape_and_interpret(word)
        if origin == "french":
            count_french += 1
            list_french.append(word)
        elif origin == "latin":
            count_latin += 1
            print(word) # Add `print` so that it prints out words of Latin origin
            list_latin.append(word)
        elif origin == "old_english":
            count_old_english += 1
            list_old_english.append(word)
        elif origin == "germanic":
            count_germanic += 1
            list_germanic.append(word)
        elif origin == "greek":
            count_greek += 1
            list_greek.append(word)
        elif origin == "other":
            list_other.append(word)
            count_other += 1

Alternatively, if you have access to the online OED, it's pretty easy to get a list by searching for current words of Latin origin, sorted by frequency. Note that many of these words also turn up when you search instead for words of French origin, since so many words have multiple etymological influences (it would be a bit strange to count them in only one direction or the other). I'm sure most people who know at least some English will recognize the top 1000 words on said list of Latin-origin words, and most educated people will recognize at least most of the next 1000 words, if not more.