Abstract
Providing text prediction systems is an important way to facilitate communication and interaction with the systems and machines. Although a text prediction system facilitates the typing process, it is helpful for people with disabilities to type or enter texts at a limited or slow speed. This means that when a user types a word, then the system suggests the next word to be chosen. It is also beneficial for people with dyslexia and those who are not good at spelling words. Besides, it can be used in entertainment as a game, for example, to determine a target word and reach it or tackle it within 10 attempts of prediction. Generally, the text prediction systems depend on a corpus. Writing every single word is time-consuming; therefore, it is vitally important to decrease time consumption by reducing efforts to input texts in the systems by offering the most probable words for the user to select. This paper addresses a survey of miscellaneous techniques toward text prediction with entertainment systems and their evaluation. It also determines a modal technique to be utilized for the next word prediction system from the perspective of ease of implementation and obtaining a good result.
Similar content being viewed by others
References
Andersen M, Nielbo KL, Schjoedt U, Pfeiffer T, Roepstorff A, Sørensen J (2018) Predictive minds in Ouija board sessions. Phenomenol Cogn Sci 17(3):1–12
Arnold L, Sébastien R, Sylvain C, Hélène PM (2016) An introduction to deep learning. In: European Symposium on Artificial Neural Networks (ESANN), Belgium. Proceedings of the European Symposium on Artificial Neural Networks (ESANN). <hal-01352061>
Ashwin MJ (2018) Next word prediction using Markov model. Available online at https://medium.com/ymedialabs-innovation/next-word-prediction-using-markov-model-570fc0475f96.
Asia LJ, Tarik AR (2018) A modified particle swarm optimization with neural network via euclidean distance. Int J Recent Contrib Eng Sci IT (iJES) 6(1):1–18. https://doi.org/10.3991/ijes.v6i1.8080
Asur S, Bernardo H (2010) Predicting the future with social media arXiv:1003.5699v1 [cs.CY].
Athira PM, Sreeja M, Reghuraj PC (2013) Architecture of an ontology-based domain specific natural language question answering System. Int J Web Semant Technol
Bahdanau D, Cho K, Bengio YB (2015) Neural machine translation by jointly learning to align and translate. arXiv preprintar arXiv:1409.0473
Banko M, Brill E (2001) Scaling to very large corpora for natural language disambiguation. In: Proc. Association for Computational Linguistics, p 26–33
Bari A, Mohamed C, Tommy J (2014) How to utilize the markov model in predictive analytics, ISBN: 978-1-118-72896-3
Bothos E, Apostolou D, Mentzas G (2010) Using social media to predict future events with agent-based markets. IEEE Intell Syst 25(6):50–58
Chakraborty C, Roy R (2012) Markov decision process based optimal gateway selection algorithm. Int J Syst Algorithms Appl (IJSAA) 48–52:2012
Chelba C, Norouzi M, Bengio S (2017) N-gram language modeling using recurrent neural network estimation. arXiv:1703.10724
Chen Q, Bofan L, Jiuhe W (2019) A comparative study of LSTM and phased LSTM for gait prediction. https://doi.org/10.5121/ijaia.2019.10405
Compton K, Kybartas B, Mateas M (2015) Tracery: an author-focused generative text tool. In: Schoenau-Fog H, Bruni LE, Louchart S, Baceviciute S (eds) ICIDS 2015. LNCS, vol 9445. Springer, Cham, pp 154–161
Dargan S, Munish K, Maruthi RA and Gulshan K (2019) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng
Garay-Vitoria N, Abascal J (2006) Text prediction systems: a survey, Universal Access in the Information Society, vol. 4, Feb. 2006, pp. 188–203
Gendron GR (2015) Natural language processing: a model to predict a sequence of words, MODSIM World 2015, 2015 Paper No. 13 pp. 1–10
Ghayoomi M, Momtazi S (2009) An overview on the existing language models for prediction systems as writing assistant tools. In: Proc. IEEE international conference on Systems, Man and Cybernetics, San Antonio, TX, Oct 11–14 2009, pp. 5083–5087
Goulart HX, Tosi MD, Gonçalves DS, Maia RF, Wachs-Lopes GA (2018) Hybrid model for word prediction using naïve bayes and latent information. arXiv:1803.00985v1 2 Mar 2018
Hall JE (2011) Guyton and hall textbook of medical physiology, 12th Ed.
Hamarashid HK (2021) Utilizing statistical tests for comparing machine learning algorithms. Kurd J Appl Res 6(1):69–74
Hamarashid HK, Saeed SA, Rashid TA (2021) Next word prediction based on the N-gram model for Kurdish Sorani and Kurmanji. Neural Comput Appl 33(9):4547–4566
Hanson R (2004) Foul play in information markets. George Mason Univ 18(2):107–126
Heller KW (2009) Learning and behavioral characteristics of students with physical, health, or multiple disabilities. In: Heller KW, Forney PE, Alberto PA, Best S, Schwartzman MN (eds) Understanding physical, health, and multiple disabilities, 2nd edn. Upper Saddle River, Merrill/Pearson, New Jersey, pp 35–50
Huang BQ, Tarik AR, Kechadi MT (2006) Multi-context recurrent neural network for time series applications. Int J Comput Intell 3(1):45–54
Jaysidh D, Nagaraja RA (2019) Real time word prediction using N-Grams model. In: International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-5 March
Jonathan S (2009) Gulliver’s travels into several remote regions of the world, Retrieved from Project Gutenberg
Kapadia S (2019) A step into statistical language modeling. Towards Data Sci
Khan A, Abid Khan M, Naveed Ali M (2009) Design of Urdu virtual keyboard. In: Proc. Conf on Language & Technology, Jan. 2009, pp. 126–130
Mahar JA, Memon GQ (2011) Probabilistic analysis of sindhi word prediction using N-Grams. Aust J Basic Appl Sci 5(5):1137–1143
Matthew W (1996) Syntactic pre-processing in single-word prediction for disabled people. Ph.D. Thesis, Department of Computer Science, University of Bristol
Mikolov T, Martin K, Lukáš B, Jan H C, Sanjeev K (2010) Recurrent neural network based language model. In: Eleventh Annual Conference of the International Speech Communication Association
Miller F (2005) Cerebral palsy. Springer, New York, Elsevier Saunders, Philadelphia
Mishne G, Glance N (2006) Predicting movie sales from blogger sentiment. In: AAAI Spring Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), 30(2):301–304
Mohanapriya M (2017) Research study on applications of artificial neural networks and elearning personalization. In: International Journal of Civil Engineering and Technology (IJCIET) ISSN Print: 0976-6308 and ISSN Online: 0976-6316
Nakatsu R, Hoshino J (2013) Entertainment computing: technologies and applications. Kluwer Academic Publishers, New York
National Center to Improve Practice (2004) NCIP Library. http://www2.edc.org/Ncip/Library/Toc.htm
Neal B (2017) What does predictive text do? People on twitter are using predictive text to write the story of their lives, & the results are equal parts poetic & Bizarre.https://www.bustle.com/p/what-does-predictive-text-do-people-on-twitter-areusing-predictive-text-to-write-the-story-of-their-lives-the-results-are-equal-partspoetic-bizarre-3226156. Accessed from 20 April 2020
Neubig G (2016) Unigram language models, Nara Institute of Science and Technology (NAIST)
Onan A (2016) Classifier and feature set ensembles for web page classification. J Inf Sci 42(2):150–165
Onan A (2017) Hybrid supervised clustering based ensemble scheme for text classification. Kybernetes.
Onan A (2018a) An ensemble scheme based on language function analysis and feature engineering for text genre classification. J Inf Sci 44(1):28–47
Onan A (2018b) Sentiment analysis on Twitter based on ensemble of psychological and linguistic feature sets. Balk J Electr Comput Eng 6(2):69–77
Onan A (2019) Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering. IEEE Access 7:145614–145633
Onan A (2020a) Mining opinions from instructor evaluation reviews: a deep learning approach. Comput Appl Eng Educ 28(1):117–138
Onan A (2020b) Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurr Comput Pract Exp 33:e5909
Onan A, Korukoğlu S (2017) A feature selection model based on genetic rank aggregation for text sentiment classification. J Inf Sci 43(1):25–38
Onan A, Korukoğlu S, Bulut H (2016a) Ensemble of keyword extraction methods and classifiers in text classification. Expert Syst Appl 57:232–247
Onan A, Korukoglu S, Bulut H (2016b) LDA-based topic modelling in text sentiment classification: an empirical analysis. Int J Comput Linguist Appl 7(1):101–119
Onan A, Korukoğlu S, Bulut H (2017) A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification. Inf Process Manag 53(4):814–833
Onan A, Toçoğlu MA (2021) A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification. IEEE Access 9:7701–7722
Panzner M, Cimiano P (2016) Comparing hidden markov models and long short term memory neural networks for learning action representations, Bielefeld University, 10122. Springer International Publishing, Cham 94–105
Prakash R (2012) Quillpad multilingual predictive transliteration system. In Proc. 24th Int. Conf on Computational Linguistics, Dec. 2012, pp. 107–114
Rassem A, Mohammed EB, Mohamed S (2017) Cross-country skiing gears classification using deep learning. arXiv:1706.08924v1[cs.CV] 27 Jun 2017
Rauterberg M (2009) Entertainment computing, social transformation and the quantum field. In: Conference Paper, https://doi.org/10.1007/978-3-642-02315-6_1 Source: DBLP
Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall Press, Upper Saddle River, NJ, USA
Shannon CE (2013) Prediction and entropy of printed English. Bell Syst Tech J 1(1):50–64
Sharma MK, Samanta D (2014) Word prediction system for text entry in Hindi. ACM Trans Asian Lang Inform Process 13(2):1–29
Singh D (2014) A critical conceptual analysis of definitions of artificial intelligence as applicable to computer engineering. In: Journal of Computer Engineering, e-ISSN: 2278-0661, p- ISSN: 2278-8727 Volume 16, Issue 2, Ver. I (Mar–Apr. 2014), PP 09–13
Sitaram A, Huberman BA (2010) Predicting the future with social media, in web intelligence and intelligent agent technology (WI-IAT). IEEE/WIC/ACM Int Conf 1(6):492–499
Swanson R, Gordon AS (2008) Say anything: a massively collaborative open domain story writing companion. In: Spierling U, Szilas N (eds) ICIDS 2008. LNCS, vol 5334. Springer, Heidelberg, pp 32–40
Tegic Communications (2004) T9 Text input for keypad devices. http://www.tegic.com
Treanor M, Alexander Z, Mirjam PE, Julian T, Gillian S, Michael C, Tommy T, Brian M, John L, Adam S (2015) AI-based game design patterns. In: Proceedings of the 10th International Conference on Foundations of Digital Games, FDG
Trnka K (2008) Adaptive language modeling for word prediction. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop, pp. 61–66
van den Bosch A (2006) Scalable classification-based word prediction and confusable correction. Traitement Autom Lang 46(2):39–63
Wang P (2008) Frontiers in artificial intelligence and applications. In: 171(1):362–373 Conference: Proceedings of the 2008 conference on Artificial General Intelligence: Proceedings of the First AGI Conference
Wolfers J, Zitzewitz E (2004) Prediction markets. J Econ Perspect 18(2):107–126
Wong KW (2008) Player adaptive entertainment computing. In: Proceedings of Computer Games & Allied Technology 08 Apr 2008, pp. 32–37
Yannakakis GN, Hallam J (2006) Towards capturing and enhancing entertainment in computer games. In: Proceedings of the 4th Hellenic Conference on Artificial Intelligence, Lecture Notes in Artificial Intelligence, vol. 3955, pp. 432–442, Heraklion, Crete, Greece, 18–20 May, 2006. Springer-Verlag
Zhang W, Skiena S (2009) Improving movie gross prediction through news analysis. In: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 30(2):301–304
Zupanc K, Bosni’c Z (2017) Automated essay evaluation with semantic analysis. Knowl Based Syst
Funding
This study was not funded.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human or animal rights
This article does not contain any studies involving animals performed by any of the authors. This article does not contain any studies involving human participants performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hamarashid, H.K., Saeed, S.A. & Rashid, T.A. A comprehensive review and evaluation on text predictive and entertainment systems. Soft Comput 26, 1541–1562 (2022). https://doi.org/10.1007/s00500-021-06691-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-06691-4