Colloquium for Computational Linguistics and Linguistics in Stuttgart

The colloquium hosts talks of external guest speakers and visitors in the (computational) linguistics departments in Stuttgart.

Typically, the talks take place on Mondays from 2pm until 3.30pm. Exceptions are marked by color.

Schedule for Winter Term 2018/19

Date Time Speaker & Title Room Host(s)
13.09.2018 11.30-13.00 Peter Turney (Independent Researcher):
Natural Selection of Words: Finding the Features of Fitness
FZI, V5.01 Dominik Schlechtweg,
Sabine Schulte im Walde
07.11.2018 Dan Jurafsky (Stanford University) Gabriella Lapesa


Peter Turney (joint work with Saif M. Mohammad):
Natural Selection of Words: Finding the Features of Fitness
(Thu, Sep 13, 2018)

According to WordNet, clarity, clearness, limpidity, lucidity, lucidness, and pellucidity are synonymous; all of them mean free from obscurity and easy to understand. Google Books Ngram Viewer shows that clearness was, by far, the most popular member of this synset (synonym set) from 1800 to 1900 AD. After 1900, the popularity of clarity rose, surpassing clearness in 1934. By 1980, clarity was, by far, the most popular member of the synset and clearness had dropped down to the low level of lucidity. We view this competition among words as analogous to biological evolution by natural selection. The leading word in a synset is like the leading species in a genus. The number of tokens of a word in a corpus corresponds to the number of individuals of a species in an environment. In both cases, natural selection determines which word or species will dominate a synset or genus. Species in a genus compete for resources in similar environments, just as words in a synset compete to represent similar meanings. We present an algorithm that is able to predict when the leading member of a synset will change, using features based on a word’s length, its characters, and its corpus statistics. The algorithm also gives some insight into what causes a synset’s leader to change. We evaluate the algorithm with 9,000 synsets, containing 22,000 words. In a 50 year period, about 12 to 14 percent of the synsets experience a change in leadership. We can predict changes 50 years ahead with an F-score of 46 percent, whereas random guessing yields 14 to 19 percent. This line of research contributes to the sciences of evolutionary theory and computational linguistics, but it may also lead to practical applications in natural language generation and understanding. Evolutionary trends in language are the result of many individuals, making many decisions about which word to use to express a given idea in a given situation. A model of the natural selection of words can help us to understand how such decisions are made, which will enable computers to make better decisions about language use. Modeling trends in words will also be useful in advertising and in analysis of social networks.

Bio: Dr. Peter Turney is an independent researcher and writer in Gatineau, Quebec. He was a Principal Research Officer at the National Research Council of Canada (NRC), where he worked from 1989 to 2014. He was then a Senior Research Scientist at the Allen Institute for Artificial Intelligence (AI2), where he worked from 2015 to 2017. He has conducted research in AI for over 27 years and has more than 100 publications with more than 18,000 citations. He received a Ph.D. in philosophy from the University of Toronto in 1988, specializing in philosophy of science. He has been an Editor of Canadian Artificial Intelligence magazine, an Editorial Board Member, Associate Editor, and Advisory Board Member of the Journal of Artificial Intelligence Research, and an Editorial Board Member of the journal Computational Linguistics. He was the Editor of the ACL Wiki from 2006, when it began, up to 2017. He was an Adjunct Professor at the University of Ottawa, School of Electrical Engineering and Computer Science, from 2004 to 2015.