Jun Gong
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Publications by Jun Gong (bibliography)
» 2008 «
Gong, Jun, Tarasewich, Peter and MacKenzie, I. Scott (2008): Improved word list ordering for text entry on ambiguous keypads. In: Proceedings of the Fifth Nordic Conference on Human-Computer Interaction 2008. pp. 152-161. Available online
“We present a design methodology for small ambiguous keypads, where input often produces a list of candidate words for a given desired word. The methodology uses context through semantic relatedness and a part-of-speech language model to improve the order of candidate words and, thus, reduce the overall number of keystrokes per character entered. Simulations yield improvements in text entry speed of about 10% and reductions in errors of about”
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» 2007 «
Gong, Jun (2007): Semantic & syntactic context-aware text entry methods. In: Ninth Annual ACM Conference on Assistive Technologies 2007. pp. 261-262. Available online
“Entering text using small devices has always been a serious problem for motor or visually impaired users. Except for their problem of word ambiguity, dictionary based predictive disambiguation (DBPD) text entry methods, such as T9", have been proved to be very efficient in terms of the number of required keystrokes. Thus, they are suitable for users with physical difficulties. Common DBPD methods only use word frequency to resolve such cases when ambiguous keystroke sequences are encountered. This paper proposes a new method, which also utilizes the semantic and syntactical contexts in the preceding texts to help disambiguate the user desired words, therefore further reduce the number of keystrokes needed from physically challenged users.”
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» 2006 «
Gong, Jun and Tarasewich, Peter (2006): A new error metric for text entry method evaluation. In: Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems 2006. pp. 471-474. Available online
“On devices such as mobile phones, text is often entered using keypads and predictive text entry techniques. Current metrics used for measuring text entry error rates have limitations in terms of the types of errors they account for, and cannot easily distinguish between different types of errors. This research proposes a new text entry error metric that addresses some of the outstanding issues that exist with current metrics. Specifically, the metric accounts in detail for the way the user handles corrections during text entry, moving beyond current keystroke level error measurement. The feasibility and usefulness of this new metric is shown through the analysis of an experiment that tests an alphabetically constrained keypad design that includes upper and lower case letters, numbers, and punctuation marks.”
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» 2005 «
Fell, Harriet, Macauslan, Joel, Gong, Jun and Ostrow, Josh (2005): visiBabble demo. In: Seventh Annual ACM Conference on Assistive Technologies 2005. pp. 200-201. Available online
“The visiBabble system responds with animations to an infant's syllable-like productions and records the acoustic-phonetic analysis. The system reinforces production of syllabic utterances associated with later language and cognitive development. This demo will show off new animated responses and recent improvements in acoustic-phonetic feature detection.”
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Gong, Jun and Tarasewich, Peter (2005): Alphabetically constrained keypad designs for text entry on mobile devices. In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems 2005. pp. 211-220. Available online
“The creation of text will remain a necessary part of human-computer interaction with mobile devices, even as they continue to shrink in size. On mobile phones, text is often entered using keypads and predictive text entry techniques, which attempt to minimize the effort (e.g., number of key presses) needed to enter words. This research presents results from the design and testing of alphabetically-constrained keypads, optimized on various word lists, for predictive text entry on mobile devices. Complete enumeration and Genetic Algorithm-based heuristics were used to find keypad designs based on different numbers of keys. Results show that alphabetically-constrained designs can be found that are close to unconstrained designs in terms of performance. User testing supports the hypothesis that novice ease of learning, usability, and performance is greater for constrained designs when compared to unconstrained designs. The effect of different word lists on keypad design and performance is also discussed.”
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Gong, Jun, Haggerty, Bryan and Tarasewich, Peter (2005): An enhanced multitap text entry method with predictive next-letter highlighting. In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems 2005. pp. 1399-1402. Available online
“Full keyboards are difficult to implement on small mobile devices, and are sometimes replaced by keypads, with multiple characters assigned to each key. The Multitap method is often used for text entry on devices with keypads. While conceptually simple, Multitap requires one or more key presses to enter each desired letter, and is relatively inefficient from the standpoint of the number of keystrokes required to enter each word. It also requires a significant amount of visual searching to find a needed letter on a key. Fortunately, newer methods based on Multitap (such as LetterWise) have been shown to increase users' text entry efficiency. This paper presents an enhanced Multitap method that uses predictive next-letter highlighting to aid visual searching. Testing shows that this method, when compared to LetterWise, offers increased text entry speeds, fewer errors, and greater novice user satisfaction.”
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Mar 21st, 2010
Changes to this page (author)
21 Feb 2010: Enabled abstracts to be shown on Jun Gong's author page.02 Jun 2009: Author was edited 12 May 2008: Author was edited
29 Jun 2007: Author was edited
29 Jun 2007: Author was edited
22 Jun 2007: Author was edited
19 Jun 2007: Author was added to the bibliography
