Predicting reading difficulty with statistical language models

2005 ◽  
Vol 56 (13) ◽  
pp. 1448-1462 ◽  
Author(s):  
Kevyn Collins-Thompson ◽  
Jamie Callan
Keyword(s):  
Language Models ◽  
Reading Difficulty ◽  
Statistical Language Models

scholarly journals Statistical Language Models of Lithuanian Based on Word Clustering and Morphological Decomposition

Informatica ◽  
2004 ◽  
Vol 15 (4) ◽  
pp. 565-580 ◽  
Author(s):  
Airenas Vaičiūnas ◽  
Vytautas Kaminskas ◽  
Gailius Raškinis
Keyword(s):  
Language Models ◽  
Morphological Decomposition ◽  
Statistical Language Models ◽  
Word Clustering

scholarly journals Huffman and Linear Scanning Methods with Statistical Language Models

2015 ◽  
Vol 31 (1) ◽  
pp. 37-50 ◽  
Author(s):  
Brian Roark ◽  
Melanie Fried-Oken ◽  
Chris Gibbons
Keyword(s):  
Language Models ◽  
Statistical Language Models ◽  
Linear Scanning

Dynamic Web log session identification with statistical language models

2004 ◽  
Vol 55 (14) ◽  
pp. 1290-1303 ◽  
Author(s):  
Xiangji Huang ◽  
Fuchun Peng ◽  
Aijun An ◽  
Dale Schuurmans
Keyword(s):  
Language Models ◽  
Web Log ◽  
Dynamic Web ◽  
Statistical Language Models

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.

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