Studying text prediction without field boundaries, Pomerleau (1995) built a typing completion aid. Without relying on note-taking properties, his system predicts a completion for the current word being typed (presumably in an editor). A connectionist network estimates the probability of a number of possible completions for the current word; the most likely, over some threshold, is offered to the user. Pomerleau tested his system with a pair of subjects over a two-week period and reports an increase in typing speed of 2% for English text and 13-18% for computer program code. This modest gain may be due to inefficiencies in the learning method, to lack of redundancy in the task, or to limitations in the user interface itself.
As a complement to earlier research, this paper reports the individual and collective accuracy of three user interface components. It reports user task time showing that the components significantly improve efficiency. This paper also clarifies an issue confounded in earlier work. If a learning interface is less effective than expected, is this due to an inherent limitation in the interface itself, or does its learning method perform inadequately? To answer the second question, other work compares two or more learning methods. In this paper, we hand-built the predictive fillin structures (cf. Table 1) and were able to assess the quality of the predictive fillin interface directly.