Text message research emerging from the fields of computer science and, in particular, of human and computer-interface interaction, raises again the issue of physical and time constraints on the otherwise informal, speech-like language. These studies are driven largely by attempts to improve the speed and efficiency of the mobile keypad, and highlight the need for a greater understanding of Txt in doing so.
2.5.1 Existing methods of text-entry
Existing features of text-entry form the starting-point for attempts to facilitate the process. The problem is that there are generally only eight buttons on a mobile keypad onto which 26 letters of the alphabet are stored, 1 which means that more than one letter must be stored on each button. One method for entering text is multi-tap where more than one key tap is required to distinguish certain letters: for example, one tap produces a, two taps produce b and three produce c: the word you requires 8 taps, and tomorrow 18. The problem with this method, as illustrated above, is the high keystroke per character, or KSPC (MacKenzie 2002). Predictive text-entry devices address this by using an in-built dictionary to predict the most likely letter required with every tap, based on the sequence so far and on frequency lists: in other words, the phone is effectively trying to guess the word as it is entered (Dunlop and Crossan 2000; Haestrup 2001).2 However, these do not generally suggest words longer than the sequence tapped so far and so cannot achieve a KSPC of less
1 That is, until the introduction of technologies such as the iphone, which use a QWERTY keyboard, and other slimline phones which can be opened up to access larger keypads.
2
Commerical examples include T9 (Tegic—Grover et al 1998), eZiTAP (ZiCorp 2002) and iTAP, used by Motorola.
26 than 1. A further problem is ambiguity, as multiple words generally correspond to any one letter sequence. If the word selected by the device is not that required by the user, they must scroll down a list of words arranged in order of their frequency in the corpora used, and this increases KSPC. The other problem is that, as linguistic and forensic investigation shows, Txt is sufficiently distinct to warrant the use of text message corpora in the compiling of such lists, and this is only beginning to be explored by predictive text device designers. LetterWise, also produced by Eatoni Ergonomics (MacKenzie et al 2001), uses letter digram possibilities to predict sequences rather than words, which avoids the problems of dealing with words not in the dictionary. However, whether or not users select predictive texting, and features of the device, will not necessarily shape Txt. Hard af Segersteg (2002) finds predictive texting responsible only for certain typos and the splitting of compound words, while Ling (2007) suggests that ‗chattiness‘ and message length were only slightly increased through its use. Similarly, the drive documented below to increase speed and efficiency will not necessarily entail alternation to the nature of Txt in the face of user variables.
2.5.2 Attempts to enhance text-entry methods
Ongoing attempts to enhance text-entry methods include dictionary-based disambiguation algorithms and keyboard remapping (Hasselgren et al 2002; Gong and Tarasewich 2005; How and Kan 2005). These studies, despite their focus on increasing speed and efficiency rather than their impact on the nature of Txt, highlight the practical need for a greater understanding of Txt, and the use of text message corpora in this growing field.
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Table 2.4 Attempts to enhance text-entry methods
Overview of study Limitations
Dictionary-based disambiguation algorithms
Hasselgren et al 2003
The authors report on a method for improving text-entry which uses bigram probabilities to enable accurate prediction of subsequent words (known as HMS). The idea is to supplement dictionaries with word and bigram probabilities. The bigram frequencies were extracted from a Swedish news corpus, Stockholm-Umea and stored in the phone‘s memory, with greater weight given to shorter words. The method was implemented on a software- simulated mobile keyboard. When compared with T9 on Nokia 3410 in a trial involving participants typing invented messages and news stories, HMS was found to use fewer keystrokes, attributed not only to enhanced prediction but the fact that it predicted words in advance.
As pointed out by the researchers, the method would show greater advantage if trained on a corpus representative of Txt, thus highlighting again the need for understanding how language is used through the medium in order to enhance the interface. In the absence of a suitable corpus, however, Hasselgren et al (2003) planned to collect data from web-based chat sites. Keyboard remapping Lesher et al 1998 Hirotaka 2003 Saied 2003
Various attempts have been made to remap the keyboard in order to reduce the likelihood of ambiguous tapped sequences, based on word dictionaries and statistics.
Raises the issue that remapped keyboards damage novice usability and user
performance.
Gong and Tarasewich 2005
The authors explore the possibility that an optimized but alphabetized design can improve user performance yet avoid damaging ease of learning. Their usability test involved three possible designs: the standard one currently in use, the unconstrained proposals (above) and the alphabetised. The optimal unconstrained design was found through a Genetic Algorithm-based heuristic technique and considered in part to be one with a high disambiguation
The wordlist used by Gong and Tarasewich (2005) was derived from spoken discourse in the BNC and, as this thesis will suggest, may not prove appropriate for texting. The authors acknowledge this, flagging the creation of
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accuracy (an increased probability that, if a sequence is tapped, the correct
word appears first time - in effect, meaning that the most frequently-occurring word must be the first choice offered by the dictionary). Although the
researchers report ‗encouraging‘ results, and increases in speed suggest that the constrained keypad was easy to learn and remember, and quicker than the unconstrained, there was little difference in terms of speed between their remapped keypad and the standard.
a ‗common SMS word list‘ for more realistic future trials.1
How and Kan 2005
This research is based on the authors‘ 10,000-word corpus of text messages (University of Singapore). Measurements first made of the efficiency of the existing methods: the standard text predicting device was found to require 74.004 taps and take 59.7 seconds, compared to 118.925 (79.3 seconds) with the multi-tap. Attempts then made to optimize text-entry by remapping the keyboard and by predictive word completion. The latter is based on
predictions from previous words (so that home can be predicted to follow at +
in) and the choices given then ordered by probability according to the text
message corpus. It was found that both methods increased text input
efficiency, and that the greatest improvement came with a combination of the two methods, although the authors admit that that experts who looked less at the screen were likely to benefit less, and that their findings rest on the assumption that users make no mistakes.
Limitations include the fact that only five messages were taken from the corpus to be used by a small number of subjects; that they only go as far as word frequency; and (for the present purposes) the fact that their corpus would be representative of
Singaporean English. Nonetheless, this work draws a little at least on naturally- occurring texted data and highlights another practical use for text message corpus analysis.
1 The experiment involved eight students who were deemed novice users of text messaging with a median age of 29 and who were asked to enter six testing phrases using the different interfaces. The phrases, obtained from large databases of sentences compiled by MacKenzie and Soukoreff (2003), all contained the same number of ambiguous words for each keypad design, but were not necessarily similar to messages normally sent. They included: ‗the generation gap gets wider‘; ‗sad to hear that news‘; ‗never mix religion and politics‘.