Soothsayer - интеллектуальная предсказывающая платформа текстового ввода.
  1.2 MB|License: GPL

Soothsayer is an intelligent predictive text entry platform. Soothsayer exploits redundant information embedded in natural languages to generate predictions. Soothsayer's modular and pluggable architecture allows its language model to be extended and customized to utilize statistical, syntactic, and semantic information sources.

A predictive text entry system attempts to improve ease and speed of textual input. Word prediction consists in computing which word tokens or word completions are most likely to be entered next. The system analyses the text already entered and combines the information thus extracted with other information sources to calculate a set of most probable tokens.

The set of most probable tokens, a list of suggestions, is displayed to the user. If the token the user intended to enter is in the list, the user selects it and it is automatically entered by the system. If the list of suggestions does not contain the desired word, the user continues entering text until the correct suggestion is offered or until the user is done entering text.

Soothsayer is fundamentally different from predictive input technologies commonly found on mobile phones, which might more accurately be described as 'disambiguating text entry' rather than 'predictive text entry' systems.

Such systems do not try to guess what the user intends to write in the future, only to determine what they most-likely intend to write in the present, given their past input.

Soothsayer, on the other hand, actively predicts the what the user intends to write, and only reverts to word completion mode if the prediction did not contain the desired token.

Soothsayer is free software and it's distributed under the term of the General Public License.

What's New in This Release:

· This release comes with a number of packaging and distribution improvements.
· The build system has received minor fixes and configurability enhancements.
· There are improved demonstration programs and improved UTF-8 support.
· Distributed text resources used to generate sample statistical data are now UTF-8 encoded.
· There are a few bugfixes and documentation updates.