Deliverable 4.6 Architecture Specification and Mock-up System

Transcription

1 DEEPTHOUGHT Hybrid Deep and Shallow Methods for Knowledge-Intensive Information Extraction Deliverable 4.6 Architecture Specification and Mock-up System The Consortium October 2003 I

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3 PROJECT REF. NO. Project acronym Project full title - Hybrid Deep and Shallow Methods for Knowledge-Intensive Information Extraction Security (distribution level) Rest. Contractual date of delivery M12: October 2003 Actual date of delivery October 2003 Deliverable number D 4.6 Deliverable name Type Status & version 0.1 Number of pages 3 WP contributing to the deliverable WP / Task responsible Other contributors Author(s) EC Project Officer Keywords Abstract Architecture Specification and Mock-up System P (Mock-Up System) WP4b WP4b Melanie Siegel, Ulrich Callmeier, Andreas Eisele Evangelia Markidou creative meeting support, document authoring A mock-up system for the creative meeting support and document authoring application. III

4 Content Content 1 Overview 2 The idea: Creativity support for document authoring and collective brainstorming 2 Methods 2 The system architecture 3 The mock-up system 4 Usage of the mock-up client 4

5 Overview When new ideas are constructed, discussed and presented the largest efforts go into looking up and combining existing pieces of information. If these tasks can be performed with greater ease and speed, the creative process can gain immensely in efficiency and therefore in range of possible results. Our approach is based on automating search and presentation while leaving the selection and the actual creative tasks to the human author. The idea: Creativity support for document authoring and collective brainstorming The creative process of document writing is usually disrupted by the lookup of information and search for pictures and tables. The application aims to perform this lookup automatically and present the search results to the author for selection. In a brainstorming meeting, a participant stands in front and develops some visual and textual representations of her ideas. The application supports this presentation by automatic retrieval of graphics and pictures. Further advantages are: Links contribute to the semi-structuring of unstructured documents, where relevant concepts are semantically tagged and linked. Dynamic concepts can be updated when the author, an editor or even the reader requests this. Methods Language technologies will be utilized to support the creative authoring process, such as: Information retrieval Information extraction Question answering Language generation Speech recognition The application will work with the core machine that combines language technologies in a novel way. We will annotate documents and graphics with natural language 2

6 descriptions and then automatically annotate these with RMRS, such that they build the basis for information search with semantics. The search is based on information retrieval and information extraction using semantic annotation. The system architecture This is an outline of the application architecture: 3

7 Modules of NUTRIMAT are: An editor for text and graphics input request sending and information insertion functionality, as well as speech recognition interaction. An information search engine with the functionality of information extraction from RMRSs and interaction with the linguistic core machine and the stored annotated texts, graphics and pictures. A document collection database that builds the basis for search and question answering. An application logic server that does the overall organization work. A speech recognition machine (Nuance) that is connected to the application. Search results can be texts, pictures or documents. They are annotated with a written description, and a URL. They are presented to the user for selection in a pull-down menu. The user can select query results. These are linked to their source of information. An additional external document containing query results is produced. In an extra menu, the user can activate a new search for updating of all results or only a selection. The mock-up system The mock-up system that is subject to this deliverable contains the text and presentation editor, the application logic server and the connection to the Nuance speech recognition. The search engine, the connection to the core architecture and the document database are replaced by connections to the question-answering system AnswerBus ( and Google s picture search. Using the mock-up application client, it is possible to inspect (and demonstrate) the intended functionality of the system. The application prototype will contain all components and connections. Usage of the mock-up client The mock-up system client can be loaded into an internet browser, such as Netscape. The user opens a new document, where it is possible to insert text and graphics fields. While writing, the user can select text portions and start the query search by pushing the search. In the case of the mock-up application, this search is sent to AnswerBus; and the search results are presented to the user. When the query changes colour to green, the user can select the search result. In document writing context, the selected text, graphic or picture is included into the text, while in the whiteboard context it is added onto the board. 4