Abstract
This work introduces a two steps methodology for the prediction of missing words in incomplete sentences. In a first step the number of candidate words is restricted to the ones fulfilling the predicted part of speech; to this aim a novel algorithm based on “posgrams” analysis is also proposed. Then, in a second step, a word prediction algorithm is applied on the reduced words set. The work quantifies the advantages in predicting a word part of speech before predicting the word itself, in terms of accuracy and execution time. The methodology can be applied in several tasks, such as Text Autocompletion, Speech Recognition and Optical Text Recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Witten, I.H., Cleary, J.G., Darragh, J.J.: The reactive keyboard: a new technology for text entry (1983)
Darragh, J.J., Witten, I.H., James, M.L.: The reactive keyboard: a predictive typing aid. Computer 23(11), 41–49 (1990)
Carlberger, A., Carlberger, J., Magnuson, T., Hunnicutt, S., Palazuelos-Cagigas, S.E., Navarro, S.A.: Profet, a new generation of word prediction: an evaluation study. In: Proceedings, ACL Workshop on Natural Language Processing for Communication Aids, pp. 23–28 (1997)
Good, I.J.: The population frequencies of species and the estimation of population parameters. Biometrika 40(3–4), 237–264 (1953)
Katz, S.M.: Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Trans. Acoust. Speech Sig. Process. 35(3), 400–401 (1987)
Jelinek, F., Mercer, R.L.: Interpolated estimation of Markov source parameters from sparse data. In: Proceedings of the Workshop on Pattern Recognition in Practice (1980)
Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: 1995 International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, pp. 181–184 (1995)
Guthrie, D., Allison, B., Liu, W., Guthrie, L., Wilks, Y.: A closer look at skip-gram modelling. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC), pp. 1–4 (2006)
Zweig, G., Burges, C.J.C.: The Microsoft Research Sentence Completion Challenge. Microsoft Research Technical report, MSR-TR-2011-129 (2011)
Gubbins, J., Vlachos, A.: Dependency language models for sentence completion. In: EMNLP, pp. 1405–1410 (2013)
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. JASIS 41(6), 391–407 (1990)
Spiccia, C., Augello, A., Pilato, G., Vassallo, G.: A word prediction methodology for automatic sentence completion. In: 2015 IEEE International Conference on Semantic Computing (ICSC), pp. 240–243 (2015)
Agostaro, F., Pilato, G., Vassallo, G., Gaglio, S.: A sub-symbolic approach to word modelling for domain specific speech recognition. In: Proceedings, IEEE 7th International Workshop on Computer Architecture for Machine Perception (CAMP), pp. 321–326 (2005)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. In: Holmes, D.E., Jain, L.C. (eds.) Innovations in Machine Learning. Studies in Fuzziness and Soft Computing, vol. 194, pp. 137–186. Springer, Heidelberg (2006)
Mnih, A., Teh, Y.W.: A fast and simple algorithm for training neural probabilistic language models. arXiv preprint (2012). arXiv:1206.6426
Pachitariu, M., Sahani, M.: Regularization and nonlinearities for neural language models: when are they needed? arXiv preprint (2013). arXiv:1301.5650
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint (2013). arXiv:1301.3781
Kučera, F., Kučera, H.: A Standard Corpus of Present-Day Edited American English, for use with Digital Computers (Brown). Brown University (1979)
Baroni, M., Bernardini, S., Ferraresi, A., Zanchetta, E.: The WaCky wide web: a collection of very large linguistically processed web-crawled corpora. Lang. Resour. Eval. 43(3), 209–226 (2009)
Semantically and Syntactically Annotated Italian Wikipedia. WaCky Corpora. University of Bologna. http://wacky.sslmit.unibo.it/doku.php?id=corpora. Accessed 1 July 2015
Calzolari, N., McNaught, J., Zampolli, A.: EAGLES Final Report: EAGLES Editors’ Introduction. EAG-EB-EI, Pisa (1996)
Tanl POS Tagset, University of Pisa. http://medialab.di.unipi.it/wiki/Tanl_POS_Tagset. Accessed 1 July 2015
Stubbs, M.: An example of frequent English phraseology: distributions, structures and functions. Lang. Comput. 62(1), 89–105 (2007)
Lindquist, H.: Corpus Linguistics and the Description of English, pp. 102–103. Edinburg University Press, Edinburgh (2009)
Lyding, V., Stemle, E., Borghetti, C., Brunello, M., Castagnoli, S., Dell’Orletta, F., Dittmann, H., Lenci, A., Pirrelli, V.: The PAISA corpus of Italian web texts. In: Proceedings of the 9th Web as Corpus Workshop (WaC-9), 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 36–43 (2014)
Doyle, A.C.: The adventures of Sherlock Holmes. Gutenberg Project, EBook #1661, Edition 12 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Spiccia, C., Augello, A., Pilato, G. (2016). A Word Prediction Methodology Based on Posgrams. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2015. Communications in Computer and Information Science, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-319-52758-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-52758-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52757-4
Online ISBN: 978-3-319-52758-1
eBook Packages: Computer ScienceComputer Science (R0)