  We introduce a dataset for studying the evolution of words, constructed from
WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of
4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to
2000 AD. We present a supervised learning algorithm that is able to predict the
future leader of a synset: the word in the synset that will have the highest
frequency. The algorithm uses features based on a word's length, the characters
in the word, and the historical frequencies of the word. It can predict change
of leadership (including the identity of the new leader) fifty years in the
future, with an F-score considerably above random guessing. Analysis of the
learned models provides insight into the causes of change in the leader of a
synset. The algorithm confirms observations linguists have made, such as the
trend to replace the -ise suffix with -ize, the rivalry between the -ity and
-ness suffixes, and the struggle between economy (shorter words are easier to
remember and to write) and clarity (longer words are more distinctive and less
likely to be confused with one another). The results indicate that integration
of the Google Books Ngram Corpus with WordNet has significant potential for
improving our understanding of how language evolves.
