Programme booklet (pdf)

PRESENTATION ABSTRACTS
Abstract
Memory-based text completion
van den Bosch, Antal
Tilburg University
The commonly accepted technology for fast and efficient word completion is the prefix
tree, or trie. As a word is keyed in, the trie can be queried for unicity points and best
guesses. We present three improvements over the normal prefix trie in experiments in
which we measure the percentage of keypresses saved on both in-domain and out-ofdomain
test text, emulating a perfectly alert user who would select correct suggestions
promptly. First, we train a suffix trie that tests backwards from the most recent
keypresses. Conditioned on first letters, the suffix trie model yields about 10% more
saved keypresses than the baseline character saving percentage on in-domain test
data. Second, the suffix trie model can be straightforwardly extended to testing on
characters of previous words. Adding this context yields another 10% increase in
character savings. Third, when we train the context-rich suffix trie model to complete
the current word and predict the next one in one go, character savings go up another
4%. In a learning experiment on Dutch texts we observe character savings of up to 44%
on in-domain test data where the baseline prefix tree savings percentage is 19%. On
out-of-domain twitter data, the prefix trie baseline of 19% is only mildly surpassed by
the suffix tree variants to 24% character savings. We develop an explanation for the
spectacular success of the suffix tree approach on in-domain data, and review the
applicability of the approach in real-world text entry contexts.
Corresponding author: Antal.vdnBosch@uvt.nl
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