Date of Original Version

2002

Type

Conference Proceeding

Rights Management

Copyright © 2002 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. © ACM, 2002. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 7th international conference on Intelligent user interfaces {1-58113-459-2 (2002)} http://doi.acm.org/10.1145/502716.502734

Abstract or Description

Multiple selections, though heavily used in file managers and drawing editors, are virtually nonexistent in text editing. This paper describes how multiple selections can automate repetitive text editing. Selection guessing infers a multiple selection from positive and negative examples provided by the user. The multiple selection can then be used for inserting, deleting, copying, pasting, or other editing commands. Simultaneous editing uses two levels of inference, first inferring a group of records to be edited, then inferring multiple selections with exactly one selection in each record. Both techniques have been evaluated by user studies and shown to be fast and usable for novices. Simultaneous editing required only 1.26 examples per selection in the user study, approaching the ideal of 1-example PBD. Multiple selections bring many benefits, including better user feedback, fast, accurate inference, novel forms of intelligent assistance, and the ability to override system inferences with manual corrections.

Comments

Copyright © 2002 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. © ACM, 2002. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 7th international conference on Intelligent user interfaces {1-58113-459-2 (2002)} http://doi.acm.org/10.1145/502716.502734

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