Academic journal article Journal of Digital Information Management

AnT&CoW: Share, Classify and Elaborate Documents by Means of Annotation

Academic journal article Journal of Digital Information Management

AnT&CoW: Share, Classify and Elaborate Documents by Means of Annotation

Article excerpt

ABSTRACT: As collective and mediated work spread out, digital data, and particularly documents, become predominant. In this paper, we describe how to support document management and especially document classification within a mechanical engineering design team working in an asynchronous and distributed way. The team members exchange comments to plan their project, to share documents, and to elaborate new documents. These comments are annotations linked together and linked to document(s). Annotations enable users to understand who is playing which role in the project and how collaborators understand a document and classify it. Observing this team led us to design an annotation tool (AnT&CoW) that we have implemented to support its collective activity. We conclude this article by focusing on the classification building functionality, based on the use of Natural Language Processing tools.

Classification and subject descriptors

H.1.2 [User/Machine Systems] Human information processing H.3.7 [Digital Libraries], H.5.2 [User Interfaces] Natural language

General Terms

Natural Language Processing, Digital Document Processing

Keywords: Annotation, Multi-viewpoint classification, Natural Language Processing, Thesauri, Document

1. Introduction

The working environment continuously changes due to people mobility, their geographical distribution, and to the increasing amount of digital data. The management of working documents used within a group (project, team, community, ...) should be adapted to follow this digital revolution. In the digital world context, when document elaboration is a collective activity, document edition and document publication is an individual activity. The important amount of digital documents requires information structuring and documents classification to improve document retrieval. Bibliographers are then pushed into further thinking about classification.

In fact, traditionally, documents archived in libraries were kind of proof document stored in order to inform, to teach, or to exemplify. But, nowadays, the documents which are used during a project in progress are temporary, transitory and shared by a team. They enable the team to build their common ground (Clark and Brennan, 1991) as well as the elaboration of new documents. Though, while working, team members have to classify permanent documents as well as fragments of documents according to a classification which is meaningful within the context of the project and within their point of view. In this context, bibliography deals with new kinds of documents: digital documents. In fact, digital documents are objects acquiring their documentary status (Buckland, 1998) as soon as they are placed in an organized, meaningful relationship with other objects (Buckland, 1998). A digital document can then be seen as a documentarized representation of an object of the world, a textual, audio-, video-, physical object.

At the same time, new management practices dealing with this broader definition of documents appear. In order to support them, we observed a mechanical engineering design team reusing a car engine to build an aero-engine. It appears that the team members are mainly using various types of annotations in order to exchange, to build solutions and to elaborate documents. Following this study, we propose an annotation tool enabling the team members to use, to classify and to elaborate documents cooperatively through annotations.

In this paper, we first present the definitions of document and digital document that we adopt, and then some basic concepts about classification in cooperation. We continue with the observations we made, and the main practices to be supported: to plan the project, to share documents, to classify documents and to elaborate documents. We finally present our annotation tool and focus on the classification functionality based on Natural Language Processing tools. …

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