How might parsing spoken data present greater challenges than parsing writing?

This is a very broad question, ultimately answered empirically by the performance of a particular parser.

However to predict performance, we might consider the types of structure that a parser is likely to find difficult and then examine a parsed corpus of speech and writing for key statistics.

Variables such as mean sentence length or main clause complexity are often cited as a proxy for parsing difficulty. However, sentence length and complexity are likely to be poor guides in this case. Spoken data is not split into sentences by the speaker, rather, utterance segmentation is a matter of transcriber/annotator choice. In order to improve performance, an annotator might simply increase the number of sentence subdivisions. Complexity ‘per sentence’ is similarly potentially misleading.

In the original London Lund Corpus (LLC), spoken data was split by speaker turns, and phonetic tone units were marked. In the case of speeches, speaker turns could be very long compound ‘run-on’ sentences. In practice, when texts were parsed, speaker turns might be split at coordinators or following a sentence adverbial.

In this discussion paper we will use the British Component of the International Corpus of English (ICE-GB, Nelson et al. 2002) as a test corpus of parsed speech and writing. It is worth noting that both components were parsed together by the same tools and research team.

A very clear difference between speech and writing in ICE-GB is to be found in the degree of self-correction. The mean rate of self-correction in ICE-GB spoken data is 3.5% of words (the rate for writing is 0.4%). The spoken genre with the lowest level of self-correction is broadcast news (0.7%). By contrast, student examination scripts have around 5% of words crossed out by writers, followed by social letters and student essays, which have around 0.8% of words marked for removal.

However, self-correction can be addressed at the annotation stage, by removing it from the input to the parser, parsing this simplified sentence, and reintegrating the output with the original corpus string. To identify issues of parsing complexity, therefore we need to consider the sentence minus any self-correction. Are there other factors that may make the input stream more difficult to parse than writing? Continue reading


The variance of Binomial distributions


Recently I’ve been working on a problem that besets researchers in corpus linguistics who work with samples which are not drawn randomly from the population but rather are taken from a series of sub-samples. These sub-samples (in our case, texts) may be randomly drawn, but we cannot say the same for any two cases drawn from the same sub-sample. It stands to reason that two cases taken from the same sub-sample are more likely to share a characteristic under study than two cases drawn entirely at random. I introduce the paper elsewhere on my blog.

In this post I want to focus on an interesting and non-trivial result I needed to address along the way. This concerns the concept of variance as it applies to a Binomial distribution.

Most students are familiar with the concept of variance as it applies to a Gaussian (Normal) distribution. A Normal distribution is a continuous symmetric ‘bell-curve’ distribution defined by two variables, the mean and the standard deviation (the square root of the variance). The mean specifies the position of the centre of the distribution and the standard deviation specifies the width of the distribution.

Common statistical methods on Binomial variables, from χ² tests to line fitting, employ a further step. They approximate the Binomial distribution to the Normal distribution. They say, although we know this variable is Binomially distributed, let us assume the distribution is approximately Normal. The variance of the Binomial distribution becomes the variance of the equivalent Normal distribution.

In this methodological tradition, the variance of the Binomial distribution loses its meaning with respect to the Binomial distribution itself. It seems to be only valuable insofar as it allows us to parameterise the equivalent Normal distribution.

What I want to argue is that in fact, the concept of the variance of a Binomial distribution is important in its own right, and we need to understand it with respect to the Binomial distribution, not the Normal distribution. Sometimes it is not necessary to approximate the Binomial to the Normal, and if we can avoid this approximation our results are likely to be stronger as a result.

Continue reading