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People cannot type as fast as they think, especially when faced with the constraints of mobile devices. There have been numerous approaches to solving this problem, including research in augmented input devices and predictive typing aids. We propose an alternative approach to predictive text entry based on commonsense reasoning. Using OMCSNet, a large-scale semantic network that aggregates and normalizes the contributions made to Open Mind Common Sense (OMCS), our system is able to show significant success in predicting words based on their first few letters. We evaluate this commonsense approach against traditional statistical methods, demonstrating comparable performance, and suggest that combining commonsense and statistical approaches could achieve superior performance. Mobile device implementations of the commonsense predictive typing aid demonstrate that such a system could be applied to just about any computing environment.

The principles of readability are in every style manual. Readability formulas are in every word processor. What is missing is the research and theory on which they stand.

Words and pictures with earlier learned labels are processed faster than words and pictures with later learned labels. This age-of-acquisition (AoA) effect has been extensively investigated in many different types of tasks. This article provides a review of these studies including picture naming, word naming, speeded word naming, word pronunciation durations, lexical decisions, eye fixation times, face recognition, and episodic memory tasks. The measurement and validity of AoA ratings is discussed, along with statistical techniques used for exploring AoA’s influence. Finally, theories of AoA are outlined, and evidence for and against the various theories is presented.

This article presents MANULEX, a Web-accessible database that provides grade-level word frequency lists of nonlemmatized and lemmatized words (48,886 and 23,812 entries, respectively) computed from the 1.9 million words taken from 54 French elementary school readers. Word frequencies are provided for four levels: first grade (G1), second grade (G2), third to fifth grades (G3–5), and all grades (G1–5). The frequencies were computed following the methods described by Carroll, Davies, and Richman (1971) and Zeno, Ivenz, Millard, and Duvvuri (1995), with four statistics at each level (F, overall word frequency; D, index of dispersion across the selected readers; U, estimated frequency per million words; and SFI, standard frequency index). The database also provides the number of letters in the word and syntactic category information. MANULEX is intended to be a useful tool for studying language development through the selection of stimuli based on precise frequency norms. Researchers in artificial intelligence can also use it as a source of information on natural language processing to simulate written language acquisition in children. Finally, it may serve an educational purpose by providing basic vocabulary lists. This article presents MANULEX, 1 the first French linguistic tool that provides grade-based frequency lists of the 1.9 million words found in first-grade, secondgrade, and third- to fifth-grade French elementary school readers. The database contains 48,886 nonlemmatized entries and 23,812 lemmatized entries. It was compiled to supply the French counterpart to such works on the

Mobile devices such as cell phones and PDAs present unique challenges and opportunities. The challenge is that user interaction is limited by small screens and keyboards (if the device has them at all!). Naive transfer of applications from full-size computers often fails because the interaction becomes too cumbersome for the user. The opportunity is that, because the device is carried by the user at all times and used in a much wider range of situations than a desk-bound computer, new possibilities emerge to provide intelligent and appropriate assistance to the user in a just-in-time fashion. We aim to address these challenges and opportunities by giving portable devices Commonsense Knowledge-- a large collection of simple facts about people and everyday life. Common Sense can reduce the need for explicit user input because the machine can make better guesses about what the user might want in a particular situation than could a conventional application. Common Sense can also make better use of contextual information like time, location, personal data, user preferences, and partial recognition, because it can better understand the implication of context for helping the user. We will illustrate our approach with descriptions of several applications we have implemented for portable devices using Open Mind, a collection of over 688,000 commonsense statements. These include a dynamic phrasebook for tourists, an assistant for searching personal social networks, and a predictive typing aid that uses semantic information rather 1 than statistics to suggest word completions.

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ral evidence for AoA effects was weak. They pointed out that it is difficult to manipulate AoA while matching stimuli along other dimensions, because AoA is naturally correlated with such variables as imageability, length, and familiarity that also affect skilled performance. These correlations derive from an obvious source: These factors affect the ease of word learning, which is what AoA norms estimate. Thus, it is very difficult to dissociate the effects of when a word was learned (AoA) from the factors that determined when it was learned. Zevin and Seidenberg noted particular difficulties associated with standard measures of word frequency. First, differences among the most commonly used norms with respect to estimates of frequency may result in failures to equate stimuli appropriately with respect to this factor. Second, the theoretical interpretation of such frequency measures is unclear. Do they measure frequency of ex31 Copyright 2004 Psychonomic Society, Inc. This research was

Brain–behavior relationships in reading acquisition are

Language www.elsevier.com/locate/jml The influence of age of acquisition in word reading and other tasks: A never ending story?