The science and technology of corpus, and corpus for science and technology

Corpus Linguistics is a young discipline. The earliest work was done in the 1960s, but corpora only began to be widely used by lexicographers and linguists in the late 1980s, by language teachers in the late 1990s, and by language students only very recently. This course in corpus linguistics was held at the Departamento de Linguistica Aplicada, E.T.S.I. de Minas, Universidad Politecnica de Madrid from June 15-19 1998. About 45 teachers registered for the course. 30% had PhDs in linguistics, 20% in literature, and the rest were doctorandi or qualified English teachers. The course was designed to introduce the use of corpora and other computational resources in teaching and research, with special reference to scientific and technological discourse in English. Each participant had a computer networked with the lecturer’s machine, whose display could be projected onto a large screen. Application programs were loaded onto the central server, and telnet and a web browser were available. COBUILD gave us permission to access the 323 million word Bank of English corpus, Mike Scott allowed us to use his Wordsmith Tools software, and Tim Johns gave us a copy of his MicroConcord program.

Issues in creating a corpus for EAP pedagogy and research

UK universities are accepting increasing numbers of students whose L1 is not English on a wide range of programmes at all levels. These students require additional support and training in English, focussing on their academic disciplines. Corpora have been used in EAP since the 1980s, mainly for research, but a growing number of researchers and practitioners have been advocating the use of corpora in EAP pedagogy, and such use is gradually increasing. This paper outlines the processes and factors to be considered in the design and compilation of an EAP corpus (e.g., the selection and acquisition of texts, metadata, data annotation, software tools and outputs, web interface, and screen displays), especially one intended to be used for teaching. Such a corpus would also facilitate EAP research in terms of longitudinal studies, student progression and development, and course and materials design. The paper has been informed by the preparatory work on the EAP subcorpus of the ACORN corpus project at Aston University. © 2007 Elsevier Ltd. All rights reserved.

Merging and ranking answers in the semantic web:the wisdom of crowds

In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.