The present invention provides a method for generating derivative words including the steps of: creating a number of derivative grammar arrays; matching the inputting character information with the derivative grammar arrays and obtaining the match derivative grammar arrays; obtaining match words from the language database according to the condition arrays of the obtained derivative grammar arrays and the inputting character information; and generating derivative words by adding the suffix alphabetic character sets of the obtained derivative grammar arrays to the ends of the words. In accordance with the established grammar rules, the words in the language database can be converted to derivative words and the derivative words do not need to be stored in the language database. Therefore, the storage space of the language database can be remarkably reduced. The present invention also provides a system for generating derivative words.

Method and system for generating derivative words

In recent years, there are huge developments in Information and Communication Technology (ICT). ICT in present form has offered to interact with the computing system in users own language. But, there are several challenges to be addressed to achieve the full advantages of ICT. One of the biggest issue is standard text input mechanism. In practice, the text input mechanism is mainly realized through the virtual keyboard. A numbers of work have been proposed to design virtual keyboard with optimum layout. So that performance of text entry can be enhanced. Still there are demand of more improvement to make an efficient text entry mechanism. In this paper, a new concept of adaptation in virtual keyboard is proposed. Our objective is to develop a user friendly interface through adaptation of key size and centroid distance between keys. As a mean of adaptation, we provide several states (keyboard) on the basis of key size and center distance between keys. Upper and lower threshold values for a performance metric of a state are defined by evaluating the states using motor movement time and searching time. If one individual user performance is in between the range of upper and lower threshold values for the keyboard then no adaptation is required. Otherwise, the keyboard is adapted according an algorithm proposed in this paper. The proposed approach is experimented with real users and results substantiate the efficacy of the approach.

Effective virtual keyboard design with size and space adaptation

This work lays down a foundation for text prediction of an inflected and under-resourced language Urdu. The interface developed is not limited to a T9 (Text on 9 keys) application used in embedded devices, which can only predict a word after typing initial characters. It is capable of predicting a word like T9 and also a sequence of word after a word in a continuous manner for fast document typing. It is based on Ngram language model. This stochastic interface deals with three N-gram levels from unary to ternary independently. The unigram mode is being in use for applications like T9, while the bigram and tri-gram modes are being in use for sentence prediction. The measures include a percentage of keystrokes saved, keystrokes until completion and a percentage of time saved during the typing. Two different corpora are merged to build a sufficient amount of data. The test data is divided into a test and a held out data equally for an experimental purpose. This whole exercise enables the QASKU system outperforms the FastType with almost 15% more saved keystrokes.

/pdf/a-stochastic-prediction-interface-for-urdu-2848bxx2qn.pdf

A Stochastic Prediction Interface for Urdu

.............................................................................................................................. ii Dedication .......................................................................................................................... iii Acknowledgements ............................................................................................................ iv Vita ..................................................................................................................................... vi List of Tables .......................................................................................................................x List of Figures .................................................................................................................... xi Chapter 1: Introduction ........................................................................................................1 Outline of the Thesis ................................................................................................3 Literature Review.....................................................................................................4 Statement of Problem .............................................................................................15 Statement of Purpose .............................................................................................15 Research Hypotheses .............................................................................................15 Assumptions ...........................................................................................................16 Delimitation ...........................................................................................................16 Rationale for the Research .....................................................................................17 Chapter 2: Method .............................................................................................................19 Participant ..............................................................................................................19

The Effects of a Word Prediction Program on the Number of Words Written by a Learner with Disabilities

We present FastType, a word prediction system for the Italian inflected language, and its user-centric interface. Fast-Type has greatly evolved from its original features. We have added new linguistic resources, implemented more effecient prediction algorithms and made a brand-new user interface. Thanks to the prediction engine upgrades, like the generation of word and Part-of-Speech n-gram collections, and to the introduction of a linear combination algorithm, performances are greatly improved. Keystroke Saving reached 48% and is now comparable to the one achieved with state-of-the-art methods for non-inflected languages. DonKey, the new human-computer interface, allows the user to benefit from automatic word completion in any application. FastType is primarily designed for users with special needs and to reduce misspellings for users with linguistic difficulties.

A word predictor for inflected languages: system design and user-centric interface

We present FastType, an innovative system for word and letter prediction for an inflected language, namely the Italian language. The system is based on combined statistical and lexical methods and it uses robust language resources. Word prediction is particularly useful to minimise keystrokes for users with special needs, and to reduce misspellings for users having limited Italian proficiency. Word prediction can be effectively used in language learning, by suggesting correct and well-formed words to non-native users. This is significant, and particularly difficult to cope with, for inflected languages such as Italian, where the correct word form depends on the context. After describing the system, we evaluate its performances and, besides the high Keystrokes Saving, we show that FastType outclasses typical word prediction limitations getting outstanding results even over a very large dictionary of words.

http://www.ijcis.info/International%20Journal%20of%20Computing%20and%20Information%20Sciences_files/Vol5N2/Vol5N2PP79-85.pdf

Largo B. Pontecorvo

Papers

An Inflected-Sensitive Letter and Word Prediction System