The New Sesquipedalian

‘Tis the season to relax with colleagues and celebrate the holidays and the end of quarter. Come on by to the Department Holiday Party, today from 3:30PM to 5:00PM in the Greenberg Room.

Join the P-Interest Group today as they hear from Timothy Dozat, who will be presenting on his computational phonology QP which models OT using neural networks. All are welcome!

Modeling OT constraints using Artificial Neural Networks

If one is to assume that OT is a plausible cognitive model of linguistic production and/or comprehension, then one must take a stance on whether constraint definitions are hardwired into humans’ brains from birth and must only be ranked, or inferred solely from the linguistic data learners are exposed to during acquisition, or some combination of the two. The strong position that all constraints are innate and the learner must only rank them is very difficult to support, suggesting that constraint definitions–as well as constraint rankings–must at least partially be learned. However, previous computational models attempting to show how constraint definitions can be learned from data have faced severe shortcomings, many stemming from the discrete nature of the the constraint definitions (e.g. assign a violation of weight w if features a and b are present in the input). I will show that allowing for continuous values in constraint definitions (e.g. assign p% of a violation of weight w if feature a is present in the input with weight v and feature b is present in the input with weight u) allows for constraints to be represented with artificial neural networks, which can make small changes to constraint definitions without radically changing their behavior or throwing them out entirely. This representation comes with all the perks of standard neural networks, to the effect that vowel harmony and constraint conjunction can be modeled with only small changes to the model.

Research on bias against speakers of African American Vernacular English in the American justice system, conducted by John Rickford and Sharese King, was featured in the most recent issue of the Stanford News Report. Read about their work here!

Dan was featured in the most recent LSA Member Spotlight, sharing his thoughts on the field of linguistics, exciting applications of computational linguistics, and the services provided by the LSA. Read it here!

In recent weeks, Dan’s work on the language of food has also earned him

• a Mother Jones article – complete with mouthwatering photos of Taco Bell’s latest foodlike creations,
• a mention in the New York Times,
• and a UCSD Computer Science & Engineering Colloquium invitation, where he presented “Extracting Social Meaning from Language: The Computational Linguistics of Food and the Speard of Innovation”. You can even watch a recording of the talk here.

Sesquikudos to former visiting scholar (during 2009-2010) Hideki Zamma, who has just received two prestigious awards:

• The Ichikawa Prize
• The ELSJ Prize from the English Linguistic Society of Japan

Other prominent linguists who have received the Ichikawa prize include Haruo Kubozono and Naoki Fukui.

This is for the published version of his 2012 Ph.D. thesis Patterns and Categories in English Suffixation and Stress Placement: A Theoretical and Quantitative Study – “a remarkable contribution to the study of English word stress that goes beyond earlier work in accounting for quantitative patterns in the English lexicon”, as Arto summarizes.