Intelligent text entry methods use techniques from artificial intelligence to improve entry rates. While these text entry methods are useful in situations when a full-sized keyboard is impractical or unavailable, they also require substantial training investment from users. We hypothesize that intelligent text entry tutors may reduce this time and effort. However, before we set out to design th...

In this paper we examine language modeling for text simplification. Unlike some text-to-text translation tasks, text simplification is a monolingual translation task allowing for text in both the input and output domain to be used for training the language model. We explore the relationship between normal English and simplified English and compare language models trained on varying amounts of t...

Classical approaches to estimate vaccine efficacy are based on the assumption that a person's risk of infection does not depend on the infection status of others. This assumption is untenable for infectious disease data where such dependencies abound. We present a novel approach to estimating vaccine efficacy in a Bayesian framework using disease transmission models. The methodology is applied ...

BACKGROUND The aim of this study was to assess the effectiveness of an EPI smartphone application (EPI app) on improving vaccination coverage in rural Sichuan Province, China. METHODS This matched-pair cluster randomized controlled study included 32 village doctors, matched in 16 pairs, and took place from 2013 to 2015. Village doctors in the intervention group used the EPI app and reminder t...

OBJECTIVE The primary aim was to compare independent and joint performance of retrieving smoking status through different sources, including narrative text processed by natural language processing (NLP), patient-provided information (PPI), and diagnosis codes (ie, International Classification of Diseases, Ninth Revision [ICD-9]). We also compared the performance of retrieving smoking strength i...

This paper studies training set sampling strategies in the context of statistical learning for text cate-gorization. It is argued sampling strategies favoring common categories is superior to uniform coverage or mistake-driven approaches, if performance is measured by globally assessed precision and recall. The hypothesis is empirically validated by examining the performance of a nearest neighb...

This paper studies training set sampling strategies in the context of statistical learning for text categorization. It is argued sampling strategies favoring common categories is superior to uniform coverage or mistake-driven approaches, if performance is measured by globally assessed precision and recall. The hypothesis is empirically validated by examining the performance of a nearest neighbo...

Identifying collocations in a text (e.g., break record) and correctly translating them (battre record vs. *casser record) represent key issues in machine translation, notably because of their prevalence in language and their syntactic flexibility. This article describes a method for discovering translation equivalents for collocations from parallel corpora, aimed at increasing the lexical cover...

A binge drinking model with the impact of media in the scale-free network is proposed. The basic reproduction number [Formula: see text] is derived by the next generation matrix method. Stability of the alcohol free equilibrium is proved by the comparison theorem. Existence and uniqueness of the alcohol present equilibrium is also obtained. Furthermore, the permanence of the system and the infl...

Unit selection speech synthesis is one of the most widely used techniques for high quality text to speech (TTS) systems. A unit selection text to speech system requires a large database of recorded and annotated speech, which contains both phonetic and prosodic variations. Designing phonetically rich and balanced speech corpora with minimum number of utterances is an intricate task. Several opt...