Character and document research in the Open Mind Initiative
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We describe the Open Mind Initiative, a framework for large scale collaborative efforts in building components of "intelligent" systems that address common sense reasoning, document and language understanding, speech and character recognition, and so on. Based on the Open Source methodology, the Open Mind Initiative allows domain specialists to contribute algorithms, tool developers to provide software infrastructure and tools, and non specialist "e-citizens" to contribute training data and information to large databases. An important challenge is to make it easy and rewarding for e-citizens to provide such information. The paper illustrates the initiative through several demonstration projects of modest scale, including some related to character and document problems, and identifies general challenges and opportunities.Keywords
This publication has 10 references indexed in Scilit:
- Macrophone: an American English telephone speech corpus for the Polyphone projectPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Efficient detection of abnormalities in large OCR databasesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Dynamic Documents and Situated Processes: Building on Local Knowledge in Field ServicePublished by Springer Nature ,1998
- What size test set gives good error rate estimates?IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
- Large-scale simulation studies in image pattern recognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Building large-scale digital librariesComputer, 1996
- Answer Garden 2Published by Association for Computing Machinery (ACM) ,1996
- CYCCommunications of the ACM, 1995
- Answer Garden: a tool for growing organizational memoryPublished by Association for Computing Machinery (ACM) ,1990
- Queries and concept learningMachine Learning, 1988