Recent publications in the field of corpus linguistics (including several in this and the previous issue of CLLT) strongly indicate that the field is on its way from a view of corpora as mere repositories of authentic data from which examples can be culled ad libitum to a methodology that analyzes linguistic phenomena systematically and exhaustively as they manifest themselves in corpus data. T...

Lancaster is a small city in northwest England. The university campus is set amidst hills, woods, and green grass, and you can enjoy watching rabbits, ducks, and squirrels. It was sunny during the day and cold in the night for all participants who attended the CL2003 conference this year in Lancaster. CL2003 is the International Conference on Corpus Linguistics, held at Lancaster University bet...

The aim of this paper is to discuss the potential benefit of corpus analysis, a (partly) empirical method from linguistics, for philosophy� ‘Corpus analysis’ is not only the name of the method, but also a rough description of it, because the method consists in analysing data taken from linguistic text corpora� In linguistics, using such text corpora is an established practice� A fair number of ...

Finding collocation candidates is one of the most important and widely used feature of corpus linguistics tools. There are many statistical association measures used to identify good collocations. Most of these measures define a formula of a association score which indicates amount of statistical association between two words. The score is computed for all possible word pairs and the word pairs...

This paper presents DefMiner, a supervised sequence labeling system that identifies scientific terms and their accompanying definitions. DefMiner achieves 85% F1 on a Wikipedia benchmark corpus, significantly improving the previous state-of-the-art by 8%. We exploit DefMiner to process the ACL Anthology Reference Corpus (ARC) – a large, real-world digital library of scientific articles in compu...