As time passes words can acquire meanings they did not previously have, such as the ‘twitter post’ usage of ‘tweet’. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the ‘pseudo-word’ technique (Schutze, 1992) to give a novel further evaluation via ‘pseudo-neologisms’. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.

Some alternatives to the standard evalb measures for parser evaluation are considered, principally the use of a tree-distance measure, which assigns a score to a linearity and ancestry respecting mapping between trees, in contrast to the evalb measures, which assign a score to a span preserving mapping. Additionally, analysis of the evalb measures suggests some further variants, concerning different normalisations, the portions of a tree compared and whether scores should be micro or macro averaged. The outputs of 6 parsing systems on Section 23 of the Penn Treebank were taken. It is shown that the ranking of the parsing systems varies as the alternative evaluation measures are used. For a fixed parsing system, it is also shown that the ranking of the parses from best to worst will vary according to whether the evalb or tree-distance measure is used. It is argued that the tree-distance measure ameliorates a problem that has been noted concerning over-penalisation of attachment errors.