International Journal of Corpus Linguistics - Volume 11, Issue 2, 2006

Corpora are an important resource for both teaching and research. Arabic lacks sufficient resources in this field, so a research project has been designed to compile a corpus, which represents the state of the Arabic language at the present time and the needs of end-users. This report presents the result of a survey of the needs of teachers of Arabic as a foreign language (TAFL) and language engineers. The survey shows that a wide range of text types should be included in the corpus. Overall, our survey confirms our view that existing corpora are too narrowly limited in source-type and genre, and that there is a need for a freely-accessible corpus of contemporary Arabic covering a broad range of text-types. We have collected and published an initial version of the Corpus of Contemporary Arabic (CCA) to meet these design issues. The CCA is freely downloadable via WWW from http://www.comp.leeds.ac.uk/arabic.

In this paper, the author introduces in detail the development and present situation of corpus linguistics in China: earlier corpora, large-scale & authentic text corpora, national corpora, speech corpora, bilingual corpora and corpora of minority languages in China. The various processing techniques for corpora are also introduced: automatic word segmentation of Chinese text, automatic PoS tagging, automatic tagging of phrase structure and automatic alignment of bilingual corpora. This paper is a bird’s-eye view of corpus linguistics of China. Finally, the author discusses several problems in present corpus research: standardization of corpus specifications, commonly sharing of language resources, knowledge properties, etc.

This article presents a method for discovering and organizing noun-verb (N-V) combinations found in a French corpus on computing. Our aim is to find N-V combinations in which verbs convey a “realization meaning” as defined in the framework of lexical functions (Mel’čuk 1996, 1998). Our approach, chiefly corpus-based, uses a machine learning technique, namely Inductive Logic Programming (ILP). The whole acquisition process is divided into three steps: (1) isolating contexts in which specific N-V pairs occur; (2) inferring linguistically-motivated rules that reflect the behaviour of realization N-V pairs; (3) projecting these rules on corpora to find other valid N-V pairs. This technique is evaluated in terms of the relevance of the rules inferred and in terms of the quality (recall and precision) of the results. Results obtained show that our approach is able to find these very specific semantic relationships (the realization N-V pairs) with very good success rates.