Oana Hamza

Multimedia indexing through multi-source and multi-language information extraction: the MUMIS project

We describe our work on information extraction from multiple sources for the Multimedia Indexing and Searching Environment, a project aiming at developing technology to produce formal annotations about essential events in multimedia programme material. The creation of a composite index from multiple and multi-lingual sources is a unique aspect of this project. The domain chosen for tuning the software components and testing is football. Our information extraction system is based on the use of finite state machinery pipelined with full semantic analysis and discourse interpretation.

Corpus linguistics and South Asian languages : corpus creation and tool development.

This paper describes the work carried out on the EMILLE Project (Enabling Minority Language Engineering), which was undertaken by the Universities of Lancaster and Sheffield. The primary resource developed by the project is the EMILLE Corpus, which consists of a series of monolingual corpora for fourteen South Asian languages, totalling more than 96 million words, and a parallel corpus of English and five of these languages. The EMILLE Corpus also includes an annotated component, namely, part-of-speech tagged Urdu data, together with twenty written Hindi corpus files annotated to show the nature of demonstrative use in Hindi. In addition, the project has had to address a number of issues related to establishing a language engineering (LE) environment for South Asian language processing, such as translating 8-bit language data into Unicode and producing a number of basic LE tools. The development of tools for EMILLE has contributed to the ongoing development of the LE architecture GATE, which has been extended to make use of Unicode. GATE thus plugs some of the gaps for language processing R&D necessary for the exploitation of the EMILLE corpora.

Using a text engineering framework to build an extendable and portable IE-based summarisation system

In this paper we describe how information extraction technology has been used to build a summarisation system in the domain of occupational health and safety. The core of the application is based on named entity recognition using pattern-action semantic grammar rules. Co-occurrence of the named entities is used as a criteria to identify the sentences to be included in the summary. The system is developed and automatically evaluated within the GATE framework, and can easily be extended or ported to new domains.

Using GATE as an Environment for Teaching NLP

In this paper we argue that the GATE architecture and visual development environment can be used as an effective tool for teaching language engineering and computational linguistics. Since GATE comes with a customisable and extendable set of components, it allows students to get hands-on experience with building NLP applications. GATE also has tools for corpus annotation and performance evaluation, so students can go through the entire application development process within its graphical development environment. Finally, it offers comprehensive Unicode-compliant multilingual support, thus allowing students to create components for languages other than English. Unlike other NLP teaching tools which were designed specifically and only for this purpose, GATE is a system developed for and used actively in language engineering research. This unique duality allows students to contribute to research projects and gain skills in embedding HLT in practical applications.

Access to Multimedia Information through Multisource and Multilanguage Information Extraction

We describe our work on information extraction from multiple sources for the Multimedia Indexing and Searching Environment, a project aiming at developing technology to produce formal annotations about essential events in multimedia programme material. The creation of a composite index from multiple and multi-lingual sources is a unique aspect of this project. The domain chosen for tuning the software components and testing is football. Our information extraction system is based on the use of finite state machinery pipelined with full semantic analysis and discourse interpretation.