Seminar in Finland about big data in linguistics

Recently I attended a seminar on big data. The discussion included what big data is in linguistics, whether it has arrived yet and whether it is even needed in all places.

It was nice to meet people like Kimmo Koskennimi (he was advisor for Master’s thesis), Antti Kantter and others with whom I worked on the Bank of Finnish terminology in arts and sciences project. I’ve collected the points I found most interesting as a summary below.

Emeritus professor Kimmo Koskenniemi started the seminar by giving examples of really big data, one of them being the Google n-gram viewer. He also raised the issue that the copyright law in Finland does not allow us to do similar things, which can be a problem for local research. He suggested (not seriously, but still) that perhaps we should move linguistics research to some other country.

Next was presented a corpus project, Arkisyn, which is a collection of annotated everyday conversation. It was funded by Kone foundation Language programme. Topic of interest was how to achieve uniform tagging when multiple people are working on the collection. Annotation guidelines were produced and the resulting work was cross-checked. Participants were encouraged to document clearly their practices: having the data is no longer enough to be able to reproduce research findings. In fact, it is also necessary for researchers to be able to understand the data and to justify their conclusions.

Toni Suutari from Kotus went more in depth in the political landscape of open data politics in Finland. He then gave some sneak peeks on the (huge) efforts at Kotus to open some of their data collections (I wonder if volunteers could help with some). Licensing is a difficult topic: for example the word list of contemporary Finnish is licensed under three different licenses as they went through the experience of finding the most suitable one. Also geographical information (paikkatieto) is a big thing now, and they are working on opening geodata on Finnish dialects and other geodata collections. I’m sure the OpenStreetMap project and many others are eagerly waiting already.

Timo Honkela, new professor of Digital information, gave an engineering perspective of big data in linguistics and what it makes possible.

Jarmo Jantunen said that human intuition is still needed, so machines are not replacing linguistics. When deciding what to study, big data can give ideas. One might end studying a small part of the big data, there is too much data to go over manually. He also went over classification scheme that helps understanding the role of data in the research. Briefly: one can gather supporting examples from the data; one can base the research on analyzing the data; or one can let the data actually drive the research.

Kristiina Jokinen (my doctoral advisor) gave a practical view of issues in multi-modal data collection: privacy issues preventing open data, encoding formats, synchronization, lightning and audio quality. Topics of interest were how to understand the interaction between so many variables (eye glaze, head position, gestures, what is being said, many people interacting) and whether what the machine sees is what a real person would notice. Deb Roy’s research (recording his son while he learned to speak) was also mentioned.

In the end there was a panel discussion. It ranged over a wide variety of topics, but one that struck me was that while some data is becoming available by itself, some kind of data is not coming unless researchers create it, and it is difficult to find resources to create it.

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