Here’s a talk by János Németh on the Prehistory of Evolutionary Linguistics. Németh demonstrates that thinking about the origins of language predates the Enlightenment by over a thousand years. Ideas that actually appear pretty modern were discussed in Ancient Greece. However, after this initial burst of progress, there was a thousand year gap where no progress was made. Why is this? Németh argues that the Judeo-Christian tradition of creation would make questioning the origins of langauge difficult for centuries.
I found his discussion of Ancient theories of langauge evolution interesting from an online resource and use them here to grind my own particular axe: the monolingual bias in linguistics (all the source material and the vast majority of the argument here comes from Németh’s thesis).
Blatant abuse of the blog for personal reasons. But what else is a blog for?
In 5 days time my band is performing at our first gig.
To promote it, I’ve written a parody of 5 Years time by Noah and the Whale
It’s about 5 million years of language evolution.
Luckily, I won’t be playing the ukulele at the gig. But there will be 4 comedy music acts, an instrument that the whole audience plays though a room full of balloons and an appearance by Dr. Gordon Freeman. It’s at the City Cafe, Edinburgh, at 7pm on the 1st July. Here’s a promotional video made using the Half-Life 3D engine.
Videos from the plenary lectures of the Evolution of Language conference are now online, here. The setup is quite impressive, with seperate videos of the speakers and slides.
The cover shot comes from one of my favourite moments of EvoLang, when Russel Gray taught us how to play a cut shot in cricket (about 43 minutes into his talk).
You can read some reviews of talks at Evolang on Replicated Typo:
Here are the results from yesterday’s Evolve a Band Name experiment. The top three names were ‘Chessclub’, ‘Cloaca’ and ‘Protons versus Neutrons’! I have to say, there is a lot of creativity evident in the data! Also, a technical oversight on my part leads to a lesson about cultural evolution…
Method
Participants were presented with 10 band names for 20 seconds. They had to memorise them and then they were asked to reproduce each one. They entered names one at a time and were prevented from entering names that they had already entered. After entering 10 names, the participants were given a score (based on Levenshtein distance). Their names were recorded and passed on to the next participant as their input. At the time of writing, 144 trials had been recorded.
The analysis was complicated by a technical oversight. I assumed that only one person would play this at a time. I was running many chains (14) in parallel, and each person is assigned to a chain when they log in, but the chain list was not updated until they finished the experiment. The result is that a single chain could split into many chains, and I had no way of automatically recovering the history of transmission. Lesson learned. If you’d like to see the raw data, look here (each line is a generation, each name separated by an underscore, first 7 lines of each file is the initial random stimuli).
Results
Here’s an analysis done by hand (click to expand).
Me and my band are looking for a new name. It’s a tough decision: we need one that’s clear and catchy. If only there was a process that took some names and made them more easily learnable. Wait, what about Iterated Learning? (see Jame’s post for a summary)
We took some band names, randomly generated from this site, and present them to you for a short amount of time. You then have to remember them. We pass the names you remember onto the next participant. Yes, you could just add your own band names, but they won’t reach the end of the chain unless they’re memorable. You can participate more than once, but not more than 10 times.
While the iterated learning experiment methodology originates with Kirby, Cornish & Smith (2008), this experiment has no mapping between signals and meanings, so is more similar to the experiments of Keelin Murray (e.g. here), Tessa Verhoef (e.g. Verhoef & de Boer, 2011, see here too) and Lili Fullerton (e.g. Fullerton, 2011). These experiments also used music as the thing that is culturally transmitted.
I’ll post the results up once we get some.
Me and my band are hosting a night of musical comedy on the 30th of June in Edinburgh. If you’d like to perform, get in touch.
References Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language Proceedings of the National Academy of Sciences, 105 (31), 10681-10686 DOI: 10.1073/pnas.0707835105
Tallerman, M. (2007). Did our ancestors speak a holistic protolanguage? Lingua, 117 (3), 579-604 DOI: 10.1016/j.lingua.2005.05.004
This is a side project I’ve been working on with Rory Turnbull and Alexis Palmer I recently presented a paper with the same title at the European Association of Computational Linguistics conference, in the Visualisation of Linguistic Patterns Workshop in Avignon (You can read about another paper presented by Gerhard Jäger in this workshop here in Sean’s post). We’ve set up an email group and a website (still a work in progress) after the conference for people wanting to explore language visualisation – hopefully, if you like this or Sean’s post you’ll join us. You can download my paper here, and the slides are available here.
With that having been said, let’s start.
This project is basically preoccupied with understanding how we can use human pattern finding abilities to deal with data where a machine or statistical analysis doesn’t help. Put another way, is there a way of showing language relatedness easily without relying on statistical techniques? And finally, can we use those visualisations to cut down on research time, and to illuminate new areas of research? We tried to approach this problem by using pixel or heat maps on WALS data. As far as we know, this is the first published paper looking at language typology, geography, and phylogeny altogether in one (although there is another paper coming out soon from Michael Cysouw’s ‘Quantitative Language Comparison’ group using similar techniques.) There are other lines of research in this vein, though. One line of recent work brings computational methods to bear on the formation and use of large typological databases, often using sophisticated statistical techniques to discover relations between languages (Cysouw, 2011; Daumé III and Campbell, 2007; Daume ́ III, 2009, among others), and another line of work uses typological data in natural language processing (Georgi et al., 2010; Lewis and Xia, 2008, for example). We are also aware of some similar work (Mayer et al., 2010; Rohrdantz et al., 2010) in visualising dif- ferences in linguistic typology, phylogeny (Mul- titree, 2009), and geographical variation (Wieling et al., 2011). Here, we just try to combine these as well as we can.
How Sparse is WALS?
The World Atlas of Language Structures has information for 2,678 languages, which is roughly a third of the amount of languages in the world – so, not bad. There are 144 available features – in the actual database, which is freely available, there are actually 192 available feature options. However, only 16% of this 2678 x 192 table is actually filled. Here’s a pretty graph (made before breakfast by Rory).
New Guinean Languages in WALS
You can see the ocean at the top of the graph above – clearly New Guinea. Each of these languages is shown by their geographical coordinates. You can also see how good each language is represented in WALS by the size of the circles. We couldn’t use all of these languages as there weren’t enough features to display in a graph. So we cut down WALS to 372 languages that all had at least 30% of their features filled. We then used two different approaches to decide which languages to place near each other in a graph.
Geographically Centred Visualisation
Geographical map centred around Yimas
One of those approaches, seen above, was to draw a 500km radius around each language, and see how many languages fit in that ring, post-cleaning. There were surprisingly little rings that had enough to fill the graph above – we ended up with around six for the amount of cleaning we did, and even here, above, you can see white spaces where there is no value for that feature for that language. After drawing this ring, we took the centre language – here, Yimas, a Trans New Guinean language – and put the closest language next to it, and then the next closest next to it on the other side, and so on. This is a problem – it means that languages close to each other might be in a totally different cardinal direction – a northern language that is 500 km away from Yimas might be situated next to a southern one. So, for these graphs, you have more pattern-seeking success if you look at the languages in the middle.
Another problem was that the colours don’t actually correspond to anything except within their feature line – so, that there is so much green here doesn’t mean that the features are related – just that many languages share similar correspondances. A final problem was that the selection of features from WALS may seem a bit random – we selected those that fit most. Now, having said those disclaimers, we can still see some cool features in these graphs.
For instance, You’ll notice the red square in the adjectival ordering features. It looks like Kewa, Alambak, Hua and Yagara might be related, either geographically or phylogenetically, as they share that feature. However, if we look at the top stripe, we can see that Alambak is blue, while the others are all orange. This stripe is meant to show language family – so, Alambak is not phylogenetically related. It is possible that it has been influenced by it’s neighbours, then. If you’re interested, here are the language family of the languages in that column: Pink = Border; Red = Trans-New Guinea; Blue = Sepik; Brown = Lower Sepik-Ramu; Purple = Torricelli; Green = Skou; and Orange = Sentani.
Another cool thing one can see in these graphs is checkerboard patterns, like is almost seen in the negative morpheme feature lines. When a feature regularly avoids the other, it shows some sort of negative correspondance. The opposite would be true for features that regularly co-occur.
Phylogenetic Centred Visualisation
Niger-Congo language family, from west to east.
In order to not just focus on geography, we also did a couple of graphs looking at phylogenetic relations. So, the one above is of the Niger-Congo family, arranged from west to east, because that looked like the most obvious way to plot them, after looking at the geographical relations on WALS – there was less distances between languages on a north to south line than from west to east. These are all in one family, so there’s no bar at the top.
There’s some pretty fun stuff to draw from this graph, too. For instance, not all features have the same diversity. Comparative constructions are a lot more variable than the order of the numeral and the noun. Second, you can see pretty interesting clusters. For instance, the top left seems to cluster quite a bit, as does the bottom right. That means that languages to the west and languages to the east are more like their neighbours, which is expected. Remember that the colours are arbitrary per feature, so the most interesting clusters are defined by not being too noisy, like we see in the top right.
There’s at least one interesting thing to take away from this graph. For Bambara and Supyire, we see clusters of shared features. Given the importance of Bambara for syntactic argumentation – it was one of the languages that challenged the Chomskyan language hierarchy by being shown to follow a context-sensitive grammar (Culy, 1985) – this means that it might be worth looking into Supyire for the same phenomena. And that is the entire point of these visualisations – finding ways of finding research easier, without depending too much on the poor stats that can be scraped from sparse databases.
Future Work
So, what I’d like to do now is keep running with this idea. Hopefully, this means working with Ethnologue, Multitree for better phylogenetic relations, with Wikipedia for better geographical coordinates, and with WALS more closely for better picking of features and languages. If you have any comments or suggestions, I’d love to hear them.
References
Christopher Culy. 1985. The complexity of the vocabulary of Bambara. Linguistics and Philosophy, 8:345–351. 10.1007/BF00630918.
Michael Cysouw. 2011. Quantitative explorations of the world-wide distribution of rare characteristics, or: the exceptionality of northwestern european languages. In Horst Simon and Heike Wiese, editors, Expecting the Unexpected, pages 411–431. De Gruyter Mouton, Berlin, DE.
Hal Daume ́ III and Lyle Campbell. 2007. A Bayesian model for discovering typological implications. In Conference of the Association for Computational Linguistics (ACL), Prague, Czech Republic.
Hal Daume ́ III. 2009. Non-parametric Bayesian model areal linguistics. In North American Chapter of the Association for Computational Linguistics (NAACL), Boulder, CO. Matthew Dryer and Martin Haspelmath, editors. 2011.
The World Atlas of Language Structures Online. Max Planck Digital Library, Munich, 2011 edition.
Michael Dunn, Simon Greenhill, Stephen Levinson, and Russell Gray. 2011. Evolved structure of language shows lineage-specific trends in word-order universals. Nature, 473(7345):79–82.
Ryan Georgi, Fei Xia, and Will Lewis. 2010. Comparing language similarity across genetic and typologically-based groupings. In Proceedings of COLING 2010.
William Lewis and Fei Xia. 2008. Automatically identifying computationally relevant typological features. In Proceedings of IJCNLP 2008.
M. Paul Lewis, editor. 2009. Ethnologue: Languages of the World. SIL International, Dallas, TX, six- teenth edition.
Richard Littauer, Rory Turnbull, Alexis Palmer (2012). Visualising Typological Relationships: Plotting WALS with Heat Maps. In Proceedings of the European Association of Computational Linguistics 2012 Workshop on the Visualization of Linguistic Patterns. Avignon, France, 23-24 April, 2012.
Thomas Mayer, Christian Rohrdantz, Frans Plank, Peter Bak, Miriam Butt, and Daniel Keim. 2010. Consonant co-occurrence in stems across languages: automatic analysis and visualization of a phonotactic constraint. In Proceedings of the 2010 Workshop on NLP and Linguistics: Finding the Common Ground, NLPLING ’10, pages 70–78, Stroudsburg, PA, USA. Association for Computa- tional Linguistics.
Multitree. 2009. Multitree: A digital library of language relationships. Institute for Language Information and Techology (LINGUIST List), Eastern Michigan University, Ypsilanti, MI, 2009 edition.
Christian Rohrdantz, Thomas Mayer, Miriam Butt, Frans Plank, and Daniel Keim. 2010. Comparative visual analysis of cross-linguistic features. In Proceedings of the International Symposium on Visual Analytics Science and Technology (EuroVAST 2010), pages 27–32. Poster paper; peer-reviewed (abstract).
Martijn Wieling, John Nerbonne, and R. Harald Baayen. 2011. Quantitative social dialectology: Explaining linguistic variation geographically and socially. PLoS ONE, 6(9):e23613, 09.
Last week in an EU:Sci podcast, Christos Christodoulopoulos challenged me to find a correlation between the basic word order of the language people use and the number of children they have. This was off the back of a number of spurious correlations with which readers of Replicated Typo will be familiar. Here are the results!
First, I do a straightforward test of whether word order is correlated with the number of children you have. This comes out as significant! I wonder if having more children hanging around affects the adaptive pressures on langauge? However, I then show that this result is undermined by discovering that there are other linguistic variables that are even better predictors.
This week’s EU:Sci podcast includes an interview with me about my work on spurious correlations between social structures and linguistic structures (see my overview post here). Christos Christodoulopoulos challenges me to find a link between the number of children a family has and the basic word order they use. Complete nonsense with an important message: Any correlation is possible.
Edit: A longer version of my interview at EU:Sci is now available online, Listen here!
A few weeks ago we launched the QHImp Qhallenge to see if chimpanzees really did have better working memories than humans. The results showed that humans were better than previously thought, but still not up to the level of chimps. Now we’ve extended the QHImp Qhallenge to test Matsuzawa’s theory that semantic links are overloading our working memory and making the task difficult. You can now play the QHImp Qhallenge with letters of the alphabet, novel symbols, shades of colour and directional arrows. We’ll be comparing performance on these tasks to the numeral task to see if fewer semantic links make the task easier.
