text entry for mobile devices using ad-hoc abbreviation
This paper presents a new method for improving the number of keystrokes and time required for text entry on mobile devices using ad-hoc abbreviations. The approach is easy-to-use because: users are not required to learn any pre-defined abbreviation rules; abbreviated input phrases are automatically detected and expanded; and it is possible to recover words that may be omitted from phrases either by accident or intention. The paper develops algorithms to detect abbreviated phrases using a Support Vector Machine trained on abbreviation examples and to expand abbreviations into complete phrases using a Hidden Markov Model learned from a text corpus. The abbreviation detector was evaluated on 3,000 word-abbreviation pairs and achieved 90\% accuracy. The abbreviation expander was evaluated on 100,000 phrases and achieved 95\% accuracy. A user study with 10 participants was performed to measure time and keystroke savings of the new approach compared to the existing iPhone text entry system. Keystroke savings were consistent amongst users, with an average decrease of 32\%. Time for input varied considerably depending on familiarity with the approach, increasing for novice users. However, experienced users achieved an average time saving of 26\%. Observations suggest that novice users were spending time thinking about how they wanted to abbreviate words.
mobile devices abbreviation support vector machine hidden markov model text input auto-completion