Author: Tim Florian Jaeger
A few days ago, I posted a summary of some recent work on syntactic alignment with Kodi Weatherholtz and Kathryn Campell-Kibler (both at The Ohio State University), in which we used the WAMI interface to collect speech data for research on language production over Amazon’s Mechanical Turk.
The first step in our OSU-Rochester collaboration on socially-mediated syntactic alignment has been submitted a couple of weeks ago. Kodi Weatherholtz in Linguistics at The Ohio State University took the lead in this project together with Kathryn Campbell-Kibler (same department) and me.
We collected spoken picture descriptions via Amazon’s crowdsourcing platform Mechanical Turk to investigate how social attitude towards an interlocutor and conflict management styles affected syntactic priming. Our paradigm combines Read the rest of this entry »
Thanks to Scott Jackson, Daniel Ezra Johnson, David Morris, Michael Shvartzman, and Nathanial Smith for the recommendations and pointers to the packages mentioned below.
- The maps, mapsextra, and maptools packages provide data and tools to plot world, US, and a variety of regional maps (see also mapproj and mapdata). This, combined with ggplot2 is also what we used in Jaeger et al., (2011, 2012) to plot distributions over world maps. Here’s an example from ggplot2 with maps.
I’ll be giving a plenary presentation at the 15th Texas Linguistic Society conference to be held in October in Austin, TX. Philippe Schlenker from NYU and David Beaver from Austin will be giving plenaries, too. The special session will be on the “importance of experimental evidence in theories of syntax and semantics, and focus on research that highlights the unique advantages of the experimental environment, as opposed to other sources of data” (from their website). Submit an abstract before May 1st and I see you there.
We’ve just submitted a perspective paper on second (and third and …) language learning as hierarchical inference that I hope might be of interest to some of you (feedback welcome).
- Pajak, B., Fine, A.B., Kleinschmidt, D., and Jaeger, T.F. submitted. Learning additional languages as hierarchical probabilistic inference: insights from L1 processing. submitted for review to Language Learning.
We’re building on Read the rest of this entry »
After a recent discussion in the lab, I’m curious to hear your speculative or informed opinions about the factors that determine someone’s proficiency in a second (or third, …) language. I’ve put together a brief survey (5-10 minutes) and if you have some time, it’d be great to hear your thoughts. Experts’ and non-experts’ opinions are equally welcome. I will post the results of the survey here.
Thank you for your participation.
Some of you asked for the slides to the Mixed effect regression class I taught at the 2013 LSA Summer Institute in Ann Arbor, MI. The class covered some Generalized Linear Model, Generalized Linear Mixed Models, extensions beyond the linear model, simulation-based approaches to assessing the validity (or power) of your analysis, data summarization and visualization, and reporting of results. The class included slides from Maureen Gillespie, Dave Kleinschmidt, and Judith Degen (see above link). Dave even came by to Ann Arbor and gave his lecture on the awesome power of plyr (and reshape etc.), which I recommend. You might also just browse through them to get an idea of some new libraries (such as Stargazer for quick and nice looking latex tables). There’s also a small example to work through for time series analysis (for beginners).
Almost all slides were created in knitr and latex (very conveniently integrated into RStudio — I know some purists hate it, but comm’on), so that the code on the slides is the code that generated the output on the slides. Feedback welcome.
This workshop on data visualization might be of interest to a lot of you. I wish I could just hop over the pond.
- Date: 24-Sept-2014 – 26-Sept-2014
- Location: Tuebingen, Germany
- Contact Person: Fabian Tomaschek (email@example.com)
- Web Site: http://avml-meeting.com
- Call Deadlines: 21 March / 18 April
The AVML-meeting offers a meeting place for all linguists from all fields who are interested in elaborating and improving their data visualization skills and methods. The meeting consists of a one-day hands-on workshop Read the rest of this entry »
This post is in reply to a recent question on in ling-R-lang by Meredith Tamminga. Meredith was wondering whether an analysis she had in mind for her project was circular, causing the pattern of results predicted by the hypothesis that she was interested in testing. I felt her question (described below in more detail) was an interesting example that might best be answered with some simulations. Reasoning through an analysis can, of course, help a lot in understanding (or better, as in Meredith’s case, anticipating) problems with the interpretation of the results. Not all too infrequently, however, I find that intuition fails or isn’t sufficiently conclusive. In those cases, simulations can be a powerful tool in understanding your analysis. So, I decided to give it a go and use this as an example of how one might approach this type of question.
In my earlier post I provided a summary of a workshop on Gradience in Grammar last week at Stanford. The workshop prompted many interesting discussion, but here I want to talk about an (admittedly long ongoing) discussion it didn’t prompt. Several of the presentations at the workshop talked about prediction/expectation and how they are a critical part of language understanding. One implication of these talks is that understanding the nature and structure of our implicit knowledge of linguistic distributions (linguistic statistics) is crucial to advancing linguistics. As I was told later, there were, however, a number of people in the audience who thought that this type of data doesn’t tell us anything about linguistics and, in particular, grammar (unfortunately, this opinion was expressed outside the Q&A session and not towards the people giving the talks, so that it didn’t contribute to the discussion). Read the rest of this entry »
A few days ago, I presented at the Gradience in Grammar workshop organized by Joan Bresnan, Dan Lassiter , and Annie Zaenen at Stanford’s CSLI (1/17-18). The discussion and audience reactions (incl. lack of reaction in some parts of the audience) prompted a few thoughts/questions about Gradience, Grammar, and to what extent the meaning of generative has survived in the modern day generative grammar. I decided to break this up into two posts. This summarizes the workshop – thanks to Annie, Dan, and Joan for putting this together!
The stated goal of the workshop was (quoting from the website):
For most linguists it is now clear that most, if not all, grammaticality judgments are graded. This insight is leading to a renewed interest in implicit knowledge of “soft” grammatical constraints and generalizations from statistical learning and in probabilistic or variable models of grammar, such as probabilistic or exemplar-based grammars. This workshop aims to stimulate discussion of the empirical techniques and linguistic models that gradience in grammar calls for, by bringing internationally known speakers representing various perspectives on the cognitive science of grammar from linguistics, psychology, and computation.
Apologies in advance for butchering the presenters’ points with my highly subjective summary; feel free to comment. Two of the talks demonstrated
The WordPress.com stats helper monkeys prepared a 2013 annual report for this blog. Here’s an excerpt:
The concert hall at the Sydney Opera House holds 2,700 people. This blog was viewed about 42,000 times in 2013. If it were a concert at Sydney Opera House, it would take about 16 sold-out performances for that many people to see it.
(well, of course, most of these were probably robots; so about 15 Sydney Opera performances in front of an all-robot audience; great). Click here to see the complete report.
The Human Language Processing (HLP/Jaeger) Lab in the Department of Brain and Cognitive Sciences at the University of Rochester is looking for PhD researchers to join the lab. Admission is through the PhD program in the Brain and Cognitive Sciences, which offers full five-year scholarship. International applications are welcome.
The University of Rochester has recently announced a Big Data initiative. As part of this initiative, there will be a large number of faculty openings over the next few years, including potential hires in computational linguistics, computational neuroscience, computational psycholinguistics, etc. The first tenure-track positions are now posted. Have a look at this list of departments and areas in which we are searching. Please spread the word.
Let me know if you have questions about these searches. If you’re a language researcher, make sure to check out the list of language faculty at Rochester (the beautiful mixture of hospital and vomit colors is about to be replaced by something more post 20th-century).