Implementing an efficient part-of-speech tagger
Software—Practice & Experience
LetterWise: prefix-based disambiguation for mobile text input
Proceedings of the 14th annual ACM symposium on User interface software and technology
KSPC (Keystrokes per Character) as a Characteristic of Text Entry Techniques
Mobile HCI '02 Proceedings of the 4th International Symposium on Mobile Human-Computer Interaction
FASTY - A Multi-lingual Approach to Text Prediction
ICCHP '02 Proceedings of the 8th International Conference on Computers Helping People with Special Needs
Semantic knowledge in word completion
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Improved word list ordering for text entry on ambiguous keypads
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Dependency parsing with reference to Slovene, Spanish and Swedish
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Investigating multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Barriers to adoption of dictionary-based text-entry methods: a field study
TextEntry '03 Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods
HMS: a predictive text entry method using bigrams
TextEntry '03 Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods
| Hi-index | 0.00 |
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%.