• Home
  • CLT seminar: Shalom Lappin - Predicting Grammaticality Judgements with Enriched Language Models

CLT seminar: Shalom Lappin - Predicting Grammaticality Judgements with Enriched Language Models

SEMINAR

I present recent experimental work on unsupervised language models trained on large corpora. We apply scoring functions to the probability distributions that the models generate for a corpus of test sentences. The functions discount the role of sentence length and word frequency, while highlighting other properties, in determining a grammaticality score for a sentence. The test sentences are annotated by Amazon Mechanical Turk crowd sourcing. We then apply support vector regression to the set of model scores for the test sentences. Some of the models and scoring functions produce encouraging Pearson correlations with the mean human judgements. I will also briefly describe current work on other corpus domains, cross domain training and testing, and grammaticality prediction in other languages. Our results provide experimental support for the view that syntactic knowledge is represented as a probabilistic system, rather than as a classical formal grammar.

Shalom Lappin King's College London and the University of Gothenburg (Joint work with Jey Han Lau and Alexander Clark)

Date: 2014-10-23 10:30 - 11:30

Location: L308, Lennart Torstenssonsgatan 8

Permalink

add to Outlook/iCal

To the top

Page updated: 2014-10-07 14:36

Send as email
Print page
Show as pdf

X
Loading