scholarly journals An Approach for a Next-Word Prediction for Ukrainian Language

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Khrystyna Shakhovska ◽  
Iryna Dumyn ◽  
Natalia Kryvinska ◽  
Mohan Krishna Kagita
Keyword(s):  
Markov Chains ◽  
Hybrid Model ◽  
Prediction System ◽  
Text Generation ◽  
Word Prediction ◽  
Current Output ◽  
Prediction Task ◽  
Limited Support ◽  
Text Prediction ◽  
Sequential Nature

Text generation, in particular, next-word prediction, is convenient for users because it helps to type without errors and faster. Therefore, a personalized text prediction system is a vital analysis topic for all languages, primarily for Ukrainian, because of limited support for the Ukrainian language tools. LSTM and Markov chains and their hybrid were chosen for next-word prediction. Their sequential nature (current output depends on previous) helps to successfully cope with the next-word prediction task. The Markov chains presented the fastest and adequate results. The hybrid model presents adequate results but it works slowly. Using the model, user can generate not only one word but also a few or a sentence or several sentences, unlike T9.

Effect of the “PAL” word prediction system on the quality and quantity of text generation

1992 ◽  
Vol 8 (4) ◽  
pp. 304-311 ◽  
Author(s):  
Alan Newell ◽  
John Arnott ◽  
Lynda Booth ◽  
William Beattie ◽  
Bernadette Brophy ◽  
...  
Keyword(s):  
Prediction System ◽  
Text Generation ◽  
Word Prediction

scholarly journals Reply Using Past Replies—A Deep Learning-Based E-Mail Client

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1353
Author(s):  
Yiwei Feng ◽  
M. Asif Naeem ◽  
Farhaan Mirza ◽  
Ali Tahir
Keyword(s):  
Deep Learning ◽  
Hybrid Model ◽  
Learning Algorithm ◽  
Inverse Document Frequency ◽  
Help Desk ◽  
Deep Learning Algorithm ◽  
Document Frequency ◽  
Text Prediction ◽  
E Mail ◽  
Gated Recurrent Unit

Email is the most common and effective source of communication for most enterprises and individuals. In the corporate sector the volume of email received daily is significant while timely reply of each email is important. This generates a huge amount of work for the organisation, in particular for the staff located in the help-desk role. In this paper we present a novel Smart E-mail Management System (SEMS) for handling the issue of E-mail overload. The Term Frequency-Inverse Document Frequency (TF-IDF) model was used for designing a Smart Email Client in previous research. Since TF-IDF does not consider semantics between words, the replies suggested by the model are not very accurate. In this paper we apply Document to Vector (Doc2Vec) and introduce a novel Gated Recurrent Unit Sentence to Vector (GRU-Sent2Vec), which is a hybrid model by combining GRU and Sent2Vec. Both models are more intelligent as compared to TF-IDF. We compare our results from both models with TF-IDF. The Doc2Vec model performs the best on predicting a response for a similar new incoming Email. In our case, since the dataset is too small to require a deep learning algorithm model, the GRU-Sent2Vec hybrid model cannot produce ideal results, whereas in our understanding it is a robust method for long-text prediction.

Measuring the Performance of a Location-Aware Text Prediction System

2015 ◽  
Vol 7 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Luís Filipe Garcia ◽  
Luís Caldas De Oliveira ◽  
David Martins De Matos
Keyword(s):  
Prediction System ◽  
Location Aware ◽  
Text Prediction

Improving word prediction for augmentative communication by using idiolects and sociolects

2014 ◽  
Vol 3 (2) ◽  
pp. 137-154 ◽  
Author(s):  
Wessel Stoop ◽  
Antal van den Bosch
Keyword(s):  
Data Collection ◽  
State Of The Art ◽  
Training Data ◽  
Language Models ◽  
Prediction System ◽  
Word Prediction ◽  
The People ◽  
The Social ◽  
Statistical Language Models ◽  
Assistive Communication

Word prediction, or predictive editing, has a long history as a tool for augmentative and assistive communication. Improvements in the state-of-the-art can still be achieved, for instance by training personalized statistical language models. We developed the word prediction system Soothsayer. The main innovation of Soothsayer is that it not only uses idiolects, the language of one individual person, as training data, but also sociolects, the language of the social circle around that person. We use Twitter for data collection and experimentation. The idiolect models are based on individual Twitter feeds, the sociolect models are based on the tweets of a particular person and the tweets of the people he often communicates with. The sociolect approach achieved the best results. For a number of users, more than 50% of the keystrokes could have been saved if they had used Soothsayer.

Online word-of-mouth as a predictor of television rating

2015 ◽  
Vol 39 (6) ◽  
pp. 831-847 ◽  
Author(s):  
Ching-Chiang Yeh
Keyword(s):  
Hybrid Model ◽  
Word Of Mouth ◽  
Prediction Models ◽  
Prediction System ◽  
Data Set ◽  
Content Type ◽  
Predictive Variable ◽  
Online Word Of Mouth ◽  
Tv Ratings ◽  
Practical Implications

Purpose – Despite the growing importance of online word-of-mouth (WOM) with regard to television (TV) ratings, it is usually excluded from early prediction models. The purpose of this paper is to investigate the role of online WOM in TV ratings predictions, focussing on whether the incorporation of online WOM could improve predictions of TV ratings, and extracts meaningful rules for decision-making. Design/methodology/approach – The author uses online WOM as a potential predictive variable in the TV ratings prediction model. The author matches a list of programs based on TV ratings for the movie channel with internet user reviews and TV ratings information from Yahoo! Movies (YM) and XYZ Company. The data set includes 71 movies, for which the data were analyzed with a hybrid model. Findings – Grey relational analysis shows that online WOM is a useful ex ante determinant of TV ratings. As a predictive variable, it plays an essential role in enhancing TV ratings predictions. The experimental results also indicate that the proposed model surpasses other listed methods in terms of both accuracy and reduction of variables, while the proposed procedure yields a set of easily understandable decision rules that facilitate the interpretation of TV ratings information. Practical implications – This paper identifies critical predictors of TV ratings and suggests that online WOM messages are a credible source. A hybrid model is developed to illustrate an intelligent prediction system for TV ratings. Originality/value – The study demonstrates the effectiveness of online WOM and its impact on TV ratings. It offers an intelligent prediction system for TV ratings with practical implications for managers within the TV industry.

Analysing performance in a word prediction system with multiple prediction methods

2007 ◽  
Vol 21 (3) ◽  
pp. 479-491
Author(s):  
Pertti Alvar Väyrynen ◽  
Kai Noponen ◽  
Tapio Seppänen
Keyword(s):  
Prediction Methods ◽  
Prediction System ◽  
Word Prediction ◽  
Multiple Prediction

THE LANGUAGE COMPONENT OF THE FASTY TEXT PREDICTION SYSTEM

2005 ◽  
Vol 19 (8) ◽  
pp. 743-781 ◽  
Author(s):  
Harald Trost ◽  
Johannes Matiasek ◽  
Marco Baroni
Keyword(s):  
Prediction System ◽  
Text Prediction
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