Data Science Tea - Linguistics Spotlight
What: tea, refreshments, presentations and conversations about topics in data science
Where: Computer Science Building Rooms 150, 151
When: 4-5:30pm Tuesday April 26
Who: You! Especially PhD & MS students, and faculty interested in data science.
Professor Kristine Yu - The learnability of tones from the speech signal
Many of the world's languages are tone languages, meaning that a change in pitch (how high or how low the voice is) causes a change in word meaning, e.g. in Mandarin, "ma" uttered with a rising pitch like in an English question (Did you go to class today?) means "hemp", but "ma" uttered with a falling pitch like in an English declarative (Yes!) means "to scold". This talk discusses initial steps in using machine learning to find out the best way to parametrize tones in an acoustic space, in order to set up the learning problem for studying how tone categories could be learned. I look forward to your comments and suggestions!
Professor Gaja Jarosz - Computational Models of Language Development: Nature vs. Nurture
Recent work on phonological learning has utilized computational modeling to investigate the role of universal biases in language development. In this talk I review the latest findings and controversies regarding the status of a particular language universal, Sonority Sequencing Principle, traditionally argued to constrain the sound structure of all human languages. I argue that explicit computational and statistical models of the language development process, when tested across languages (English, Mandarin, Korean, and Polish) allow us to disentangle the often correlated predictions of competing hypotheses, and suggest a crucial role for this universal principle in language learning.
Professor Brian Dillon - Serial vs. parallel structure-building in syntactic comprehension
In this talk I give a brief overview to theories of human syntactic comprehension. An important theoretical question in this area is whether the human sentence processor creates and maintains a single syntactic description of a sentence, or if instead it maintains multiple, parallel parses of the input. This question is of wide-ranging theoretical importance for theories of human syntactic processing, but the empirical data that distinguish serial from parallel parsing behavior are unclear at best (Gibson & Pearlmutter, 2000; Lewis, 2000). In this talk I reexamine this theoretical question, and present in progress work with Matt Wagers (Linguistics, UC Santa Cruz) that uses tools from mathematical psychology (Signal Detection Theory) to derive novel empirical predictions that distinguish serial vs. parallel processing, a first step on the road to reevaluating this old, but perptually important, theoretical question.
