The NLP researcher or application-builder often wonders “what corpus should I use, or should I build one of my own? If I build one of my own, how will I know if I have done a good job?” Currently there is very little help available for them. They are in need of a framework for evaluating corpora. We develop such a framework, in relation to corpora which aim for good coverage of ‘general language’. The task we set is automatic creation of a publication-quality collocations dictionary. For a sample of 100 headwords of Czech and 100 of English, we identify a gold standard dataset of (ideally) all the collocations that should appear for these headwords in such a dictionary. The datasets are being made available alongside this paper. We then use them to determine precision and recall for a range of corpora, with a range of parameters.

Sublanguages are varieties of language that form “subsets” of the general language, typically exhibiting particular types of lexical, semantic, and other restrictions and deviance. SubCAT, the Sublanguage Corpus Analysis Toolkit, assesses the representativeness and closure properties of corpora to analyze the extent to which they are either sublanguages, or representative samples of the general language. The current version of SubCAT contains scripts and applications for assessing lexical closure, morphological closure, sentence type closure, over-represented words, and syntactic deviance. Its operation is illustrated with three case studies concerning scientific journal articles, patents, and clinical records. Materials from two language families are analyzed―English (Germanic), and Bulgarian (Slavic). The software is available at sublanguage.sourceforge.net under a liberal Open Source license.

Word sketches are one-page, automatic, corpus-based summaries of a word's grammatical and collocational behaviour. In this paper we present word sketches for Turkish. Until now, word sketches have been generated using a purpose-built finite-state grammars. Here, we use an existing dependency parser. We describe the process of collecting a 42 million word corpus, parsing it, and generating word sketches from it. We evaluate the word sketches in comparison with word sketches from a language independent sketch grammar on an external evaluation task called topic coherence, using Turkish WordNet to derive an evaluation set of coherent topics.

For many languages there are no large, general-language corpora available. Until the web, all but the institutions could do little but shake their heads in dismay as corpus-building was long, slow and expensive. But with the advent of the Web it can be highly automated and thereby fast and inexpensive. We have developed a ‘corpus factory’ where we build large corpora. In this paper we describe the method we use, and how it has worked, and how various problems were solved, for eight languages: Dutch, Hindi, Indonesian, Norwegian, Swedish, Telugu, Thai and Vietnamese. We use the BootCaT method: we take a set of 'seed words' for the language from Wikipedia. Then, several hundred times over, we * randomly select three or four of the seed words * send as a query to Google or Yahoo or Bing, which returns a 'search hits' page * gather the pages that Google or Yahoo point to and save the text. This forms the corpus, which we then * 'clean' (to remove navigation bars, advertisements etc) * remove duplicates * tokenise and (if tools are available) lemmatise and part-of-speech tag * load into our corpus query tool, the Sketch Engine The corpora we have developed are available for use in the Sketch Engine corpus query tool.

Word sketches are part of the Sketch Engine corpus query system. They represent automatic, corpus-derived summaries of the words’ grammatical and collocational behaviour. Besides the corpus itself, word sketches require a sketch grammar, a regular expression-based shallow grammar over the part-of-speech tags, to extract evidence for the properties of the targeted words from the corpus. The paper presents a sketch grammar for German, a language which is not strictly configurational and which shows a considerable amount of case syncretism, and evaluates its accuracy, which has not been done for other sketch grammars. The evaluation focuses on NP case as a crucial part of the German grammar. We present various versions of NP definitions, so demonstrating the influence of grammar detail on precision and recall.

Cleaneval is a shared task and competitive evaluation on the topic of cleaning arbitrary web pages, with the goal of preparing web data for use as a corpus for linguistic and language technology research and development. The first exercise took place in 2007. We describe how it was set up, results, and lessons learnt

Most MT lexicography is devoted to developing rules of the kind, “in context C, translate source-language word S as target-language word T”. Very many such rules are required, producing them is laborious, and MT companies standardly spend large sums on it. We present the WASP-Bench, a lexicographer's workstation for the rapid and semi-automatic development of such rule-sets. The WASP-Bench makes use of a large source-language corpus and state-of-the-art techniques for Word Sense Disambiguation. We show that the WSD accuracy is on a par with the best results published to date, with the advantage that the WASP-Bench, unlike other high- performance systems, does not require a sense-disambiguated training corpus as input. The WASP-Bench is designed to fit readily with MT companies' working practices, as it may be used for as many or as few source language words as present disambiguation problems for a given target.