{"id":646,"date":"2008-09-27T21:39:39","date_gmt":"2008-09-28T01:39:39","guid":{"rendered":"http:\/\/languagelog.ldc.upenn.edu\/nll\/?p=646"},"modified":"2008-09-27T21:50:44","modified_gmt":"2008-09-28T01:50:44","slug":"a-correlate-of-animacy","status":"publish","type":"post","link":"https:\/\/languagelog.ldc.upenn.edu\/nll\/?p=646","title":{"rendered":"A correlate of animacy"},"content":{"rendered":"

For the last couple of days, I've been in Chicago at an NSF-sponsored workshop on \"animacy and information status annotation\", organized by Annie Zaenen, Cathy O'Connor and Gregory Ward.<\/p>\n

A traditional and characteristic example of the role of animacy in English syntax is the way it affects the choice between the two ways of expressing genitive relations, X's Y vs. the Y of X.\u00a0 In general, the apostrophe-s structure is said to be preferred for animate Xs, while inanimates tend to go with the of-phrase. I'm a believer in Yogi Berra'a dictum that you can observe a lot just by watching, especially if you count things. So during the wrap-up session this afternoon, I thought I'd try using some simple web searches to probe this animacy-genitive relationship.<\/p>\n

<\/p>\n

Since we can only do string searches — the web isn't parsed — I wanted to find a reliable string-wise proxy for finding the head of the of-phrase, and decided to try entities with single-word names. Specifically, I decided to try people, companies, countries, and chemical elements. And to pin it down further, I decided to try some contemporary American politicians and some IT companies. (I started with four members in each category, but then I added one more country and one more element.)<\/p>\n

Thus the search string \"google's\" got 17 million hits, while the search string \"of google\" got 13.4 million hits, for a ratio of about 1.27. I repeated the analogous searches for the other 17 names. The results are kind of cute:<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n__'s<\/strong><\/td>\nof __<\/strong><\/td>\nratio<\/strong><\/td>\n<\/tr>\n
Giuliani<\/span><\/td>\n1.14M<\/span><\/td>\n140K<\/span><\/td>\n8.14<\/span><\/td>\n<\/tr>\n
McCain<\/span><\/td>\n23.6M<\/span><\/td>\n4.42M<\/span><\/td>\n5.34<\/span><\/td>\n<\/tr>\n
Clinton<\/span><\/td>\n11.6M<\/span><\/td>\n2.81M<\/span><\/td>\n4.13<\/span><\/td>\n<\/tr>\n
Obama<\/span><\/td>\n26M<\/span><\/td>\n7.6M<\/span><\/td>\n3.42<\/span><\/td>\n<\/tr>\n
Apple<\/td>\n22.6M<\/td>\n9.39M<\/td>\n2.41<\/td>\n<\/tr>\n
IBM<\/td>\n6.97M<\/td>\n4.03M<\/td>\n1.73<\/td>\n<\/tr>\n
Microsoft<\/td>\n35.5M<\/td>\n21.3M<\/td>\n1.67<\/td>\n<\/tr>\n
Google<\/td>\n17M<\/td>\n13.4M<\/td>\n1.27<\/td>\n<\/tr>\n
America<\/span><\/td>\n113M<\/span><\/td>\n131M<\/span><\/td>\n0.863<\/span><\/td>\n<\/tr>\n
Canada<\/span><\/td>\n26.8M<\/span><\/td>\n60.5M<\/span><\/td>\n0.443<\/span><\/td>\n<\/tr>\n
Thailand<\/span><\/td>\n3.96M<\/span><\/td>\n11.8M<\/span><\/td>\n0.336<\/span><\/td>\n<\/tr>\n
England<\/span><\/td>\n10.9M<\/span><\/td>\n48M<\/span><\/td>\n0.227<\/span><\/td>\n<\/tr>\n
Belgium<\/span><\/td>\n799K<\/span><\/td>\n6.31M<\/span><\/td>\n0.127<\/span><\/td>\n<\/tr>\n
lithium<\/span><\/td>\n60.7K<\/span><\/td>\n1.73M<\/span><\/td>\n0.035<\/span><\/td>\n<\/tr>\n
arsenic<\/span><\/td>\n21.7K<\/span><\/td>\n1.19M<\/span><\/td>\n0.018<\/span><\/td>\n<\/tr>\n
silicon<\/span><\/td>\n50.9K<\/span><\/td>\n5.93M<\/span><\/td>\n0.0086<\/span><\/td>\n<\/tr>\n
hydrogen<\/span><\/td>\n45.3K<\/span><\/td>\n9.44M<\/span><\/td>\n0.0048<\/span><\/td>\n<\/tr>\n
cadmium<\/span><\/td>\n4.01K<\/span><\/td>\n2.2M<\/span><\/td>\n0.0018<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The first thing to observe is that the four categories are non-overlapping. (I'm sure that if you added more names, you'd discover some overlaps; but still.)<\/p>\n

Within categories, the ordering makes a certain amount of sense. Lithium is a drug as well as an element, which makes it partly a member of another class that might be expected to rank higher on this scale (whatever this scale is, exactly). Arsenic is a poison. And it's not a surprise that America comes out as apparently the most animate of the countries.<\/p>\n

The ranking of the politicians and the IT firms puzzled me a bit at first. But then I conjectured that perhaps the scale is measuring a sort of unpredictable agency — what you might call the \"Maverick factor\".\u00a0 Maybe by changing all their connectors every 18 months, and building laptops that freeze up<\/a> every other time you plug them into a projector, Apple gains in (this measure) of animacy, just as a cantankerous old car comes to seem more alive every time you have to beg it to start. On this theory Google and Obama, by being more reliable, seem a bit less agentive.<\/p>\n

[Of course, there are many other factors that could (and doubtless did) skew the counts enough to change the genitive-ratio rankings — for example, \"Google\" is often used as a modifier, in phrases like \"Google Analytics\" or \"Google News\", and as a result, strings like \"new version of Google Analytics\" increase the count of the \"google of\" search. Still, it's nice that the ratio held up in a semi-sensible way over 3-4 orders of magnitude.]<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

For the last couple of days, I've been in Chicago at an NSF-sponsored workshop on \"animacy and information status annotation\", organized by Annie Zaenen, Cathy O'Connor and Gregory Ward. A traditional and characteristic example of the role of animacy in English syntax is the way it affects the choice between the two ways of expressing […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[19],"tags":[],"class_list":["post-646","post","type-post","status-publish","format-standard","hentry","category-semantics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=\/wp\/v2\/posts\/646","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=646"}],"version-history":[{"count":0,"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=\/wp\/v2\/posts\/646\/revisions"}],"wp:attachment":[{"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=646"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=646"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/languagelog.ldc.upenn.edu\/nll\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=646"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}