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Predictive text entry using syntax and semantics

Ganslandt, Sebastian; Jörwall, Jakob and Nugues, Pierre LU (2009) In Proceedings of the 11th International Conference on Parsing Technologies (IWPT '09) p.37-48
Abstract
Most cellular telephones use numeric keypads, where texting is supported by dictionaries and frequency models. Given a key sequence, the entry system recognizes the matching words and proposes a rank-ordered list of candidates. The ranking quality is instrumental to an effective entry.



This paper describes a new method to enhance entry that combines syntax and language models. We first investigate components to improve the ranking step: language models and semantic relatedness. We then introduce a novel syntactic model to capture the word context, optimize ranking, and then reduce the number of keystrokes per character (KSPC) needed to write a text. We finally combine this model with the other components and we discuss... (More)
Most cellular telephones use numeric keypads, where texting is supported by dictionaries and frequency models. Given a key sequence, the entry system recognizes the matching words and proposes a rank-ordered list of candidates. The ranking quality is instrumental to an effective entry.



This paper describes a new method to enhance entry that combines syntax and language models. We first investigate components to improve the ranking step: language models and semantic relatedness. We then introduce a novel syntactic model to capture the word context, optimize ranking, and then reduce the number of keystrokes per character (KSPC) needed to write a text. We finally combine this model with the other components and we discuss the results.



We show that our syntax-based model reaches an error reduction in KSPC of 12.4% on a Swedish corpus over a baseline using word frequencies. We also show that bigrams are superior to all the other models. However, bigrams have a memory footprint that is unfit for most devices. Nonetheless, bigrams can be further improved by the addition of syntactic models with an error reduction that reaches 29.4%. (Less)
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in
Proceedings of the 11th International Conference on Parsing Technologies (IWPT '09)
pages
37 - 48
language
English
LU publication?
yes
id
050b698b-73c4-4859-a211-abe6933095e2 (old id 1668773)
alternative location
http://dl.acm.org/citation.cfm?id=1697244
date added to LUP
2010-12-10 12:22:47
date last changed
2016-04-16 11:56:31
@inproceedings{050b698b-73c4-4859-a211-abe6933095e2,
  abstract     = {Most cellular telephones use numeric keypads, where texting is supported by dictionaries and frequency models. Given a key sequence, the entry system recognizes the matching words and proposes a rank-ordered list of candidates. The ranking quality is instrumental to an effective entry.<br/><br>
<br/><br>
This paper describes a new method to enhance entry that combines syntax and language models. We first investigate components to improve the ranking step: language models and semantic relatedness. We then introduce a novel syntactic model to capture the word context, optimize ranking, and then reduce the number of keystrokes per character (KSPC) needed to write a text. We finally combine this model with the other components and we discuss the results.<br/><br>
<br/><br>
We show that our syntax-based model reaches an error reduction in KSPC of 12.4% on a Swedish corpus over a baseline using word frequencies. We also show that bigrams are superior to all the other models. However, bigrams have a memory footprint that is unfit for most devices. Nonetheless, bigrams can be further improved by the addition of syntactic models with an error reduction that reaches 29.4%.},
  author       = {Ganslandt, Sebastian and Jörwall, Jakob and Nugues, Pierre},
  booktitle    = {Proceedings of the 11th International Conference on Parsing Technologies (IWPT '09)},
  language     = {eng},
  pages        = {37--48},
  title        = {Predictive text entry using syntax and semantics},
  year         = {2009},
}