Assessing the difficulty of the text input task for minority languages

Currently no framework exists to evaluate or rank the complexity of the text input task on a per orthography bases. We present on the challenges which must be addressed by a cross-language text input assessment framework. We discuss relevant user experience (UX) considerations for keyboard layouts and unique actions undertaken in the communicative act of ‘entextualizing’ language (typing). We follow previous work which focuses on majority language text input methods (Bellman & MacKenzie 1998, Castellucci & MacKenzie 2013, MacKenzie 1992, 2002, 2007, MacKenzie & Soukoreff 2002, Soukoreff & MacKenzie 2001, 2003a, b) and apply considerations for minority language orthographies - especially those orthographies which overtly mark tone and other distinctions via diacritics. The ability to communicate with electronic text based devices is important in this era of globalization. Many minority language users often find it difficult to type in their languages because of the way that orthography/language specific characters are accessed through existing keyboard layouts ([Author] 2012). The keyboard layout is an essential component in text input both on mobile touch screen and traditional devices. Barriers to efficiently using text in digital mediums has a wide impact on language vitality, by affecting the way that language users perceive their language’s viability in the 21st century context. The text input challenge has been often acknowledged by minority language users (Esizmetor 2009: 13, Zheltov 2005). Perceptions about the need to be able to use text based digital communication devices has sufficiently challenged language communities leading some to change their orthographies (Boerger 2007: 133: South Pacific, Cooper 2005: 149, 160: Central Asia, Jany 2010: Americas). Simons and Lewis (2010) describe the social practice of literacy (EGIDS levels four and five) as a sign of a healthy language. A text input device which does not intuitively work for language users can be seen as discriminating and be a reason for speakers to choose to not use their language in digital mediums (Trosterud 2012). We propose a language agnostic framework for text input analysis for the benefit of language development efforts and software developers alike. References: [Author]. 2012. Keyboard layout as part of language documentation: the case of the Meꞌphaa and Chinantec keyboards. Paper presented at CRASSH Language Endangerment: Methodologies and New Challenges, Cambridge, UK. Bellman, Tom & I. Scott MacKenzie. 1998. A Probabilistic Character Layout Strategy for Mobile Text Entry. Proceedings of Graphics Interface '98, 168-76. Toronto: Canadian Information Processing Society. Boerger, Brenda H. 2007. Natqgu Literacy: Capturing Three Domains for Written Language Use. Language Documentation & Conservation 1.2: 126–53. Castellucci, Steven J. & I. Scott MacKenzie. 2013. Gathering Text Entry Metrics on Android Devices. Proceedings of the International Conference on Multimedia and Human- Computer Interaction - MHCI 2013, 120.1-.8. Ottawa, Canada: International ASET, Inc. Cooper, Gregory. 2005. Issues in the Development of a Writing System for the Kalasha Language. Ph.D dissertation, Macquarie University. Esizmetor, David Oshorenoya. 2009. What Orthography for Naijá? Paper presented at Conference on Nigerian Pidgin, University of Ibadan, Nigeria. Jany, Carmen. 2010. Orthography Design for Chuxnabán Mixe. Language DocumentatIon & ConservatIon 4.1: 231-53. Lewis, M. Paul & Gary F. Simons. 2010. Assessing endangerment: Expanding Fishman's GIDS. Revue Roumaine de Linguistique 55.2: 103–20. MacKenzie, I. Scott. 1992. Fitts' law as a research and design tool in human-computer interaction. Human-Computer Interaction 7, 91-139. MacKenzie, I. Scott. 2002. Introduction to this special issue on text entry for mobile computing. Human-Computer Interaction 17.2-3: 141-5. MacKenzie, I. Scott. 2007. Evaluation of text entry techniques. In I. Scott MacKenzie & Kumiko Tanaka-Ishii (eds.), Text entry systems: Mobility, accessibility, universality, 75-101. San Francisco, CA: Morgan Kaufmann. MacKenzie, I. Scott & R. William Soukoreff. 2002. A Character-level Error Analysis Technique for Evaluating Text Entry Methods. A character-level error analysis technique for evaluating text entry methods. Proceedings of the Second Nordic Conference on Human- Computer Interaction -- NordiCHI 2002, 241-4. New York: ACM. Soukoreff, R. William & I. Scott MacKenzie. 2001. Measuring errors in text entry tasks: An application of the Levenshtein string distance statistic. Extended Abstracts of the ACM Conference on Human Factors in Computing Systems - CHI 2001, 319-20. New York: ACM. Soukoreff, R. William & I. Scott MacKenzie. 2003a. Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric. Paper presented at Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ft. Lauderdale, Florida, USA. Soukoreff, R. William & I. Scott MacKenzie. 2003b. Input-based Language Modeling in the Design of High Performance Text Input Techniques. Proceedings of Graphics Interface 2003 (CIPS, Canadian Human-Computer Communication Society), 89-96. Halifax, Nova Scotia: A K Peters. Trosterud, Trond. 2012. A restricted freedom of choice: Linguistic diversity in the digital landscape. Nordlyd (Tromsø University Working Papers on Language and Linguistics) 39.2: 89-104. Zheltov, Pavel V. 2005. Minority languages and computerization. The situation in the Russian Federation. OGMIOS 3.3: 8-11

Thematic Annotation: extracting concepts out of documents

Contrarily to standard approaches to topic annotation, the technique used in this work does not centrally rely on some sort of -- possibly statistical -- keyword extraction. In fact, the proposed annotation algorithm uses a large scale semantic database -- the EDR Electronic Dictionary -- that provides a concept hierarchy based on hyponym and hypernym relations. This concept hierarchy is used to generate a synthetic representation of the document by aggregating the words present in topically homogeneous document segments into a set of concepts best preserving the document's content. This new extraction technique uses an unexplored approach to topic selection. Instead of using semantic similarity measures based on a semantic resource, the later is processed to extract the part of the conceptual hierarchy relevant to the document content. Then this conceptual hierarchy is searched to extract the most relevant set of concepts to represent the topics discussed in the document. Notice that this algorithm is able to extract generic concepts that are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure

Investigating five key predictive text entry with combined distance and keystroke modelling

This paper investigates text entry on mobile devices using only five-keys. Primarily to support text entry on smaller devices than mobile phones, this method can also be used to maximise screen space on mobile phones. Reported combined Fitt's law and keystroke modelling predicts similar performance with bigram prediction using a five-key keypad as is currently achieved on standard mobile phones using unigram prediction. User studies reported here show similar user performance on five-key pads as found elsewhere for novice nine-key pad users

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201

Optimizing Human Performance in Mobile Text Entry

Although text entry on mobile phones is abundant, research strives to achieve desktop typing performance "on the go". But how can researchers evaluate new and existing mobile text entry techniques? How can they ensure that evaluations are conducted in a consistent manner that facilitates comparison? What forms of input are possible on a mobile device? Do the audio and haptic feedback options with most touchscreen keyboards affect performance? What influences users' preference for one feedback or another? Can rearranging the characters and keys of a keyboard improve performance? This dissertation answers these questions and more. The developed TEMA software allows researchers to evaluate mobile text entry methods in an easy, detailed, and consistent manner. Many in academia and industry have adopted it. TEMA was used to evaluate a typical QWERTY keyboard with multiple options for audio and haptic feedback. Though feedback did not have a significant effect on performance, a survey revealed that users' choice of feedback is influenced by social and technical factors. Another study using TEMA showed that novice users entered text faster using a tapping technique than with a gesture or handwriting technique. This motivated rearranging the keys and characters to create a new keyboard, MIME, that would provide better performance for expert users. Data on character frequency and key selection times were gathered and used to design MIME. A longitudinal user study using TEMA revealed an entry speed of 17 wpm and a total error rate of 1.7% for MIME, compared to 23 wpm and 5.2% for QWERTY. Although MIME's entry speed did not surpass QWERTY's during the study, it is projected to do so after twelve hours of practice. MIME's error rate was consistently low and significantly lower than QWERTY's. In addition, participants found MIME more comfortable to use, with some reporting hand soreness after using QWERTY for extended periods

Analyzing the Impact of Cognitive Load in Evaluating Gaze-based Typing

Gaze-based virtual keyboards provide an effective interface for text entry by eye movements. The efficiency and usability of these keyboards have traditionally been evaluated with conventional text entry performance measures such as words per minute, keystrokes per character, backspace usage, etc. However, in comparison to the traditional text entry approaches, gaze-based typing involves natural eye movements that are highly correlated with human brain cognition. Employing eye gaze as an input could lead to excessive mental demand, and in this work we argue the need to include cognitive load as an eye typing evaluation measure. We evaluate three variations of gaze-based virtual keyboards, which implement variable designs in terms of word suggestion positioning. The conventional text entry metrics indicate no significant difference in the performance of the different keyboard designs. However, STFT (Short-time Fourier Transform) based analysis of EEG signals indicate variances in the mental workload of participants while interacting with these designs. Moreover, the EEG analysis provides insights into the user's cognition variation for different typing phases and intervals, which should be considered in order to improve eye typing usability.Comment: 6 pages, 4 figures, IEEE CBMS 201