Tiny Corpus Applications with Transformation-Based Error-Driven Learning : Evaluations of Automatic Grammar Induction and Partial Parsing of SaiSiyat

This paper reports a preliminary result on automatic grammar induction based on the framework of Brill and Markus (1992) and binary-branching syntactic parsing of Esperanto and SaiSiyat (a Formosan language). Automatic grammar induction requires large corpus and is found implausible to process endangered minor languages. Syntactic parsing, on the contrary, needs merely tiny corpus and works along with corpora segmented by intonation-unit which results in high accuracy

Integrating hedonic quality for user experience modelling

Research on user experience (UX) has attracted much attention from designers. Additionally, hedonic quality can help designers understand user interaction (such as attractive, original and innovative) when they experience a product. Realising the user's interaction state is a significant step for designers to optimise product design and service. Previous UX modelling lacks exploration in user interaction state. Also, the lack of user interaction state factor will reduce the accuracy of the UX modelling. In this paper, we explore the interaction value of online customer review and introduce a new approach to integrating hedonic quality for UX modelling. Firstly, extracting word list from online customer review; Secondly, hedonic quality words are extracted from the word list and added as a hedonic quality part to UX modelling; Thirdly, we compared the analysis result with our previous study for the conclusion. This research combines hedonic quality with UX modelling to enrich modelling in the field of UX for the first time. The proposed data collection method is superior to the traditional collection methods in hedonic quality studies. Extracting hedonic quality factors from online customer reviews can in-depth provide reflections for designers to improve their product design. Furthermore, it also explored the valuable relationship between UX and online customer reviews to provide proactive thinking in user strategy and design activitie

Exploiting user experience from online customer reviews for product design

Understanding user experience (UX) becomes more important in a market-driven design paradigm because it helps designers uncover significant factors, such as user’s preference, usage context, product features, as well as their interrelations. Conventional means, such as questionnaire, survey and self-report with predefined questions and prompts, are used to collect information about users’ experience during various UX studies. However, such data is often limited and restricted by initial setups, and they won’t easily allow designers to identify all critical elements such as user profile, context, related product features, etc. Meanwhile, with widely accessible social media, the volume and velocity of customer-generated data are fast-increasing. While it is generally acknowledged that such data contains important elements in understanding and analyzing UX, extracting them to assist product design remains a challenging issue. In this study, how UX data underlying product design can be isolated and restored from customer online reviews is examined. A faceted conceptual model is proposed to elucidate the crucial factors of UX, which serves as an operational mechanism connecting to product design. A methodology of establishing a UX knowledge base from customer online reviews is then proposed to support UX-centered design activities, which consists of three stages, i.e., UX discovery to extract UX data from a single review, UX data integration to group similar data and UX network formalization to build up the causal dependencies among UX groups. Using a case study on smart mobile phone reviews, examples of UX data discovered are demonstrated and both customers and designers concerned key product features and usage situations are exemplified. This study explores the feasibility to discover valuable UX data as well as their relations automatically for product design and business strategic plan by analyzing a large volume of customer online data

Automatically acquiring phrase structure using distributional analysis

In this paper, we present evidence that the acquisition of the phrase structure of a natural language is possible without supervision and with a very small initial grammar. We describe a language learner that extracts distributional information from a corpus annotated with parts of speech and is able to use this extracted information to accurately parse short sentences. The phrase structure learner is part of an ongoing project to determine just how much knowledge of language can be learned solely through distributional analysis. 1