A vector space model for automatic indexing
Communications of the ACM
Knowledge Discovery in Grammatically Analysed Corpora
Data Mining and Knowledge Discovery
Using linguistic, world, and contextual knowledge in a plan recognition model of dialogue
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Cell Phone Culture: Mobile Technology in Everyday Life
Cell Phone Culture: Mobile Technology in Everyday Life
A phrase-based statistical model for SMS text normalization
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Normalizing SMS: are two metaphors better than one?
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
An unsupervised model for text message normalization
CALC '09 Proceedings of the Workshop on Computational Approaches to Linguistic Creativity
SMS based interface for FAQ retrieval
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
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SMS dictation by voice is becoming a viable alternative providing a convenient method for texting in a variety of environments. Contextual knowledge should be used to improve performance. We propose to add topic knowledge as part of the contextual awareness of both texting partners during SMS conversations. Topics can be used for speech applications, if the relation between the conversed topics and the choice of words in SMS dialogs is measurable. In this study, we collected an SMS corpus, developed a topic annotation scheme, and built a topic hierarchy in a tree structure. We validated our topic assignments and tree structure by the Agglomerative Information Bottleneck method, which also proved the measurability of the interrelation between topics and wording. To quantify this relation we propose a naïve classification method based on the calculation of topic distinctive word lists and compare the classifiers' topic recognition capabilities for SMS dialogs with unigram language models. The results demonstrate that the relation between topic and wording is significant and can be integrated into SMS dictation.