The appeal of the Ngram Viewer was immediately obvious to scholars in the digital humanities, linguistics, and lexicography, but it wasn't just specialists who got pleasure out of generating graphs showing how key words and phrases have waxed and waned over the past few centuries. Here at The Atlantic, Alexis Madrigal collected a raft of great examples submitted by readers, some of whom pitted "vampire" against "zombie," "liberty" against "freedom," and "apocalypse" against "utopia." A Tumblr feed brought together dozens more telling graphs. If nothing else, playing with Ngrams became a time suck of epic proportions.
As of today, the Ngram Viewer just got a whole lot better. For starters, the text corpus, already mind-bogglingly big, has become much bigger: The new edition extracts data from more than eight million out of the 20 million books that Google has scanned. That represents about six percent of all books ever published, according to Google's estimate. The English portion alone contains about half a trillion words, and seven other languages are represented: Spanish, French, German, Russian, Italian, Chinese, and Hebrew.
The Google team, led by engineering manager Jon Orwant, has also fixed a great deal of the faulty metadata that marred the original release. For instance, searching for modern-day brand names -- like Microsoft or, well, Google -- previously revealed weird, spurious bumps of usage around the turn of the 20th century, but those bumps have now been smoothed over thanks to more reliable dating of books. (...)
Orwant, in introducing the new version on the Google blog, reckoned that these new advanced features will be of primary interest to lexicographers. "But then again," Orwant writes, "that's what we thought about Ngram Viewer 1.0," which he says has been used more than 45 million times since it was launched nearly two years ago. I was given early access to the new version, and after playing with it for a few days I can see how the part-of-speech tags and mathematical operators could appeal to dabblers as well as hard-core researchers (who can download the raw data to pursue even more sophisticated analyses beyond the pretty graphs).
by Ben Zimmer, The Atlantic | Read more:
