We have released a new manuscript, “Resolving References to Objects in Photographs using the Words-As-Classifiers Model” (Schlangen, Zarrieß, Kennington, 2015). This uses the model introduced in (Kennington et al. 2015) and (Kennington and Schlangen 2015) and applies it to a (pre-processed) corpus of real-world images. The idea of this model is that (a certain aspect of) the meaning of words can be captured by modelling them as classifiers on perceptual data, specifying how well the perceptual representation “fits” to the word. In previous work, we tested this on our puzzle domain. With this paper, we’ve moved this to a more varied domain (with much more data), which also makes it possible to explore some other things. (For example, it turns out that you can easily get a representation from this with which you can do similarity computations as in distributional semantics.)
This being on arXiv is also a bit of an experiment for us, as this is the first time that we’ve released something before it has been “officially” published. We hope that we get some comments this way that we can perhaps incorporate into a version that will be submitted to more traditional venues. So, comment away!