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Predictive text is aninput technology used where one key or button represents many letters, such as on the physicalnumeric keypads ofmobile phones and inaccessibility technologies. Each key press results in aprediction rather than repeatedly sequencing through the same group of "letters" it represents, in the same, invariable order. Predictive text could allow for an entireword to be input by single keypress. Predictive text makes efficient use of fewer device keys to inputwriting into atext message, ane-mail, anaddress book, acalendar, and the like.
The most widely used, general, predictive text systems areT9,iTap,eZiText, andLetterWise/WordWise. There are many ways to build a device that predicts text, but all predictive text systems have initial linguistic settings that offer predictions that are re-prioritized to adapt to each user. Thislearning adapts, by way of the device memory, to a user'sdisambiguating feedback that results in corrective key presses, such as pressing a "next" key to get to the intention. Most predictive text systems have a user database to facilitate this process.
Theoretically the number of keystrokes required per desired character in the finished writing is, on average, comparable to using akeyboard. This is approximately true providing that all words used are in its database, punctuation is ignored, and no input mistakes are made typing or spelling.[1] The theoretical keystrokes per character, KSPC, of a keyboard is KSPC=1.00, and of multi-tap is KSPC=2.03. Eatoni'sLetterWise is a predictive multi-tap hybrid, which when operating on a standard telephone keypad achieves KSPC=1.15 for English.
The choice of which predictive text system is the best to use involves matching the user'spreferred interface style, the user's level of learned ability to operate predictive text software, and the user's efficiency goal. There are various levels of risk in predictive text systems, versusmulti-tap systems, because the predicted text that is automatically written that provide the speed and mechanical efficiency benefit, could, if the user is not careful to review, result in transmitting misinformation. Predictive text systems take time to learn to use well, and so generally, a device's system has user options to set up the choice of multi-tap or of any one of several schools of predictive text methods.
Short message service (SMS) permits amobile phone user to sendtext messages (also called messages, SMSes, texts, and txts) as a short message. The most common system of SMS text input is referred to as "multi-tap". Using multi-tap, a key is pressed multiple times to access the list of letters on that key. For instance, pressing the "2" key once displays an "a", twice displays a "b" and three times displays a "c". To enter two successive letters that are on the same key, the user must either pause or hit a "next" button. A user can type by pressing an alphanumeric keypad without looking at the electronic equipment display. Thus, multi-tap is easy to understand, and can be used without any visual feedback. However, multi-tap is not very efficient, requiring potentially many keystrokes to enter a single letter.
In ideal predictive text entry, all words used are in the dictionary, punctuation is ignored, no spelling mistakes are made, and no typing mistakes are made. The ideal dictionary would include all slang,proper nouns,abbreviations,URLs, foreign-language words and other user-unique words. This ideal circumstance gives predictive text software the reduction in the number of key strokes a user is required to enter a word. The user presses the number corresponding to each letter and, as long as the word exists in the predictive text dictionary, or is correctly disambiguated by non-dictionary systems, it will appear. For instance, pressing "4663" will typically be interpreted as the wordgood, provided that a linguistic database in English is currently in use, though alternatives such ashome,hood andhoof are also valid interpretations of the sequence of key strokes.
The most widely used systems of predictive text are Tegic'sT9, Motorola'siTap, and theEatoni Ergonomics'LetterWise and WordWise. T9 and iTap use dictionaries, but Eatoni Ergonomics' products uses a disambiguation process, a set of statistical rules to recreate words from keystroke sequences. All predictive text systems require a linguistic database for every supported input language.
Traditional disambiguation works by referencing adictionary of commonly used words, thoughEatoni offers a dictionaryless disambiguation system.
In dictionary-based systems, as the user presses the number buttons, an algorithm searches the dictionary for a list of possible words that match the keypress combination, and offers up the most probable choice. The user can then confirm the selection and move on, or use a key to cycle through the possible combinations.
A non-dictionary system constructs words and other sequences of letters from the statistics of word parts. To attempt predictions of the intended result of keystrokes not yet entered, disambiguation may be combined with aword completion facility.
Either system (disambiguation or predictive) may include a user database, which can be further classified as a "learning" system when words or phrases are entered into the user database without direct user intervention. The user database is for storing words or phrases which are not well disambiguated by the pre-supplied database. Some disambiguation systems further attempt to correct spelling, format text or perform other automatic rewrites, with the risky effect of either enhancing or frustrating user efforts to enter text.
The predictive text andautocomplete technology was invented out of necessities by Chinese scientists and linguists in the 1950s to solve the input inefficiency of theChinese typewriter,[2] as the typing process involved finding and selecting thousands oflogographic characters on a tray,[3] drastically slowing down the word processing speed.[4][5]
The actuating keys of the Chinese typewriter created by Lin Yutang in the 1940s included suggestions for the characters following the one selected. In 1951, the Chinese typesetter Zhang Jiying arranged Chinese characters in associative clusters, a precursor of modern predictive text entry, and broke speed records by doing so.[6] Predictive entry of text from a telephone keypad has been known at least since the 1970s (Smith and Goodwin, 1971). Predictive text was mainly used to look up names in directories over the phone, until mobile phone text messaging came into widespread use.
On a typical phone keypad, if users wished to typethe in a "multi-tap" keypad entry system, they would need to:
Meanwhile, in a phone with predictive text, they need only:
The system updates the display as each keypress is entered, to show the most probable entry. In this example, prediction reduced the number of button presses from five to three. The effect is even greater with longer words and those composed of letters later in each key's sequence.
A dictionary-based predictive system is based on hope that the desired word is in the dictionary. That hope may be misplaced if the word differs in any way from common usage—in particular, if the word is not spelled or typed correctly, is slang, or is aproper noun. In these cases, some other mechanism must be used to enter the word. Furthermore, the simple dictionary approach fails withagglutinative languages, where a single word does not necessarily represent a single semantic entity.
Predictive text is developed and marketed in a variety of competing products, such asNuance Communications'sT9. Other products includeMotorola'siTap;Eatoni Ergonomic'sLetterWise (character, rather than word-based prediction); WordWise (word-based prediction without a dictionary); EQ3 (aQWERTY-like layout compatible with regular telephone keypads);Prevalent Devices'sPhraze-It;Xrgomics'TenGO (a six-key reduced QWERTY keyboard system);Adaptxt (considers language, context, grammar and semantics);Lightkey (a predictive typing software for Windows);Clevertexting (statistical nature of the language, dictionaryless, dynamic key allocation); andOizea Type (temporal ambiguity); Intelab's Tauto; WordLogic's Intelligent Input Platform™ (patented, layer-based advanced text prediction, includes multi-language dictionary, spell-check, built-in Web search); Google'sGboard.
Words produced by the same combination of keypresses have been called "textonyms";[7] also "txtonyms";[8] or "T9onyms" (pronounced "tynonyms"/ˈtaɪnənɪmz/[7]), though they are not specific to T9. Selecting the wrong textonym can occur with no misspelling or typo, if the wrong textonym is selected by default or user error. As mentioned above, the key sequence 4663 on a telephone keypad, provided with a linguistic database in English, will generally be disambiguated as the wordgood. However, the same key sequence also corresponds to other words, such ashome,gone,hoof,hood and so on. For example, "Are you home?" could be rendered as "Are you good?" if the user neglects to alter the default 4663 word. This can lead to misunderstandings; for example sequence 735328 might correspond to eitherselect or itsantonymreject. A 2010 brawl that led tomanslaughter was sparked by a textonym error.[9] Predictive text choosing a default different from that which the user expects has similarities with theCupertino effect, by whichspell-check software changes a spelling to that of an unintended word.
Textonyms were used asMillennial slang; for example, the use of the wordbook to meancool, sincebook was the default in predictive text systems that assumed it was more frequent thancool.[10] This is related tocacography.
Textonyms are not the only issue limiting the effectiveness of predictive text implementations. Another significant problem are words for which the disambiguation produces a single, incorrect response. The system may, for example, respond withBlairf upon input of 252473, when the intended word wasBlaise orClaire, both of which correspond to the keystroke sequence, but are not, in this example, found by the predictive text system. Whentypos or misspellings occur, they are very unlikely to be recognized correctly by a disambiguation system, though error correction mechanisms may mitigate that effect.
Values [of KSPC] for English range from about 10 for methods using only cursor keys and a SELECT key to about 0.5 for word prediction techniques. It is demonstrated that KSPC is useful for a priori analyses, thereby supporting the characterisation and comparison of text-entry methods before labour-intensive implementations and evaluations
