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

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.

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 Word prediction in computational historical linguistics

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Peter Dekker ◽  
Willem Zuidema
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Historical Linguistics ◽  
Data Representation ◽  
Target Language ◽  
Prediction Methods ◽  
Word Prediction ◽  
Tree Reconstruction ◽  
Source Language ◽  
Machine Learning Model

In this paper, we investigate how the prediction paradigm from machine learning and Natural Language Processing (NLP) can be put to use in computational historical linguistics. We propose word prediction as an intermediate task, where the forms of unseen words in some target language are predicted from the forms of the corresponding words in a source language. Word prediction allows us to develop algorithms for phylogenetic tree reconstruction, sound correspondence identification and cognate detection, in ways close to attested methods for linguistic reconstruction. We will discuss different factors, such as data representation and the choice of machine learning model, that have to be taken into account when applying prediction methods in historical linguistics. We present our own implementations and evaluate them on different tasks in historical linguistics.

Word Prediction System for Text Entry in Hindi

2014 ◽  
Vol 13 (2) ◽  
pp. 1-29 ◽  
Author(s):  
Manoj Kumar Sharma ◽  
Debasis Samanta
Keyword(s):  
Prediction System ◽  
Text Entry ◽  
Word Prediction

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.

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