Hyeju Jang

Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs

This paper investigates the use of conversational agents to scaffold on-line collaborative learning discussions through an approach called Academically Productive Talk (APT). In contrast to past work on dynamic support for collaborative learning, where agents were used to elevate conceptual depth by leading students through directed lines of reasoning (Kumar & Rosé, IEEE Transactions on Learning Technologies, 4(1), 2011), this APT-based approach uses generic prompts that encourage students to articulate and elaborate their own lines of reasoning, and to challenge and extend the reasoning of their teammates. This paper integrates findings from a series of studies across content domains (biology, chemistry, engineering design), grade levels (high school, undergraduate), and facilitation strategies. APT based strategies are contrasted with simply offering positive feedback when the students themselves employ APT facilitation moves in their interactions with one another, an intervention we term Positive Feedback for APT engagement. The pattern of results demonstrates that APT based support for collaborative learning can significantly increase learning, but that the effect of specific APT facilitation strategies is context specific. It appears the effectiveness of each strategy depends upon factors such as the difficulty of the material (in terms of being new conceptual material versus review) and the skill level of the learner (urban public high school vs. selective private university). In contrast, Feedback for APT engagement does not positively impact learning. In addition to an analysis based on learning gains, an automated conversation analysis technique is presented that effectively predicts which strategies are successfully operating in specific contexts. Implications for design of more agile forms of dynamic support for collaborative learning are discussed.

Text mining for medical documents using a hidden markov model

We propose a semantic tagger that provides high level concept information for phrases in clinical documents. It delineates such information from the statements written by doctors in patient records. The tagging, based on Hidden Markov Model (HMM), is performed on the documents that have been tagged with Unified Medical Language System (UMLS), Part-of-Speech (POS), and abbreviation tags. The result can be used to extract clinical knowledge that can support decision making or quality assurance of medical treatment.

Semantic Tagging for Medical Knowledge Tracking

We propose a semantic tagger that provides high level concept information for phrases in clinical documents, which enriches medical information tracking system that support decision making or quality assurance of medical treatment. In this paper, we have tried to deal with patient records written by doctors rather than well-formed documents such as Medline abstracts. In addition, annotating clinical text on phrases semantically rather than syntactically has been attempted, which are at higher level granularity than words that have been the target for most tagging work

Conversational Metaphors in Use: Exploring the Contrast between Technical and Everyday Notions of Metaphor

Much computational work has been done on identifying and interpreting the meaning of metaphors, but little work has been done on understanding the motivation behind the use of metaphor. To computationally model discourse and social positioning in metaphor, we need a corpus annotated with metaphors relevant to speaker intentions. This paper reports a corpus study as a first step towards computational work on social and discourse functions of metaphor. We use Amazon Mechanical Turk (MTurk) to annotate data from three web discussion forums covering distinct domains. We then compare these to annotations from our own annotation scheme which distinguish levels of metaphor with the labels: nonliteral, conventionalized, and literal. Our hope is that this work raises questions about what new work needs to be done in order to address the question of how metaphors are used to achieve social goals in interaction.

Personal Information Access Using Proactive Search and Mobile Hypertext

A new framework combining proactive search with mobile hypertext lets mobile phone users avoid text entry and tailors search results to suit the context. Mobile phones and devices are evolving steadily, adding increasingly advanced capabilities. Todays smart phones, for example, have PClike functionality with complete operating system software that provides a platform for developers. Even phones without an operating system often have such functions as email, Internet navigation, music players, e-book readers, and GPS, in addition to the usual text messaging (short message service, SMS), camera, and phone. So now, besides holding telephone numbers and text messages, mobile phones also allow access to video clips, calendars, task lists, notes, and so on.

Personalized healthcare through intelligent gadgets

An intelligent gadget is a wearable platform which is reconfigurable, scalable, and component-based and which can be equipped, carried as a personal accessory, or in a certain case, implanted internally into a body. Various kinds of personal information can be gathered with intelligent gadgets, and that information is used to provide specially personalized services to people in the ubiquitous computing environment. In this paper, we show a personalized healthcare service through intelligent gadgets. A service based on intelligent gadgets can be built intuitively and easily with a context representation language, called the intelligent gadget markup language (IGML) based on the event-condition-action (ECA) rule. The inherent nature of extensibility, not only environmental information but also physiological information can be specified as a context in IGML and can be dealt with an intelligent gadget with ease. It enables intelligent gadgets to be adopted to many different kinds of personalized healthcare services.

Metaphor Detection with Topic Transition, Emotion and Cognition in Context

Metaphor is a common linguistic tool in communication, making its detection in discourse a crucial task for natural language understanding. One popular approach to this challenge is to capture semantic incohesion between a metaphor and the dominant topic of the surrounding text. While these methods are effective, they tend to overclassify target words as metaphorical when they deviate in meaning from its context. We present a new approach that (1) distinguishes literal and non-literal use of target words by examining sentence-level topic transitions and (2) captures the motivation of speakers to express emotions and abstract concepts metaphorically. Experiments on an online breast cancer discussion forum dataset demonstrate a significant improvement in metaphor detection over the state-of-theart. These experimental results also reveal a tendency toward metaphor usage in personal topics and certain emotional contexts.

MEMORIA: personal memento service using intelligent gadgets

People would like to record what they experience to recall their earlier events, share with others, or even hand down to their next generations. In addition, our environment has been getting digitalized and the cost of storing media has been being reduced. This has led research on the life log that stores peoples daily life. The research area includes collecting experience information conveniently, manipulating and recording the collected information efficiently, and retrieving and providing the stored information to users effectively. This paper describes a personalized memory augmentation service, called MEMORIA, that collects, stores and retrieves various kinds of experience information in real time using the specially designed wearable intelligent gadget (WIG).

Developing, evaluating, and refining an automatic generator of diagnostic multiple choice cloze questions to assess children's comprehension while reading

We describe the development, pilot-testing, refinement, and four evaluations of Diagnostic Question Generator (DQGen), which automatically generates multiple choice cloze (fill-in-the-blank) questions to test childrens comprehension while reading a given text. Unlike previous methods, DQGen tests comprehension not only of an individual sentence but of the context preceding it. To test different aspects of comprehension, DQGen generates three types of distractors: ungrammatical distractors test syntax; nonsensical distractors test semantics; and locally plausible distractors test inter-sentential processing. (1) A pilot study of DQGen 2012 evaluated its overall questions and individual distractors, guiding its refinement into DQGen 2014. (2) Twenty-four elementary students generated 200 responses to multiple choice cloze questions that DQGen 2014 generated from forty-eight stories. In 130 of the responses, the child chose the correct answer. We define the distractiveness of a distractor as the frequency with which students choose it over the correct answer. The incorrect responses were consistent with expected distractiveness: twenty-seven were plausible, twenty-two were nonsensical, fourteen were ungrammatical, and seven were null. (3) To compare DQGen 2014 against DQGen 2012, five human judges categorized candidate choices without knowing their intended type or whether they were the correct answer or a distractor generated by DQGen 2012 or DQGen 2014. The percentage of distractors categorized as their intended type was significantly higher for DQGen 2014. (4) We evaluated DQGen 2014 against human performance based on 1,486 similarly blind categorizations by twenty-seven judges of sixteen correct answers, forty-eight distractors generated by DQGen 2014, and 504 distractors authored by twenty-one humans. Surprisingly, DQGen 2014 did significantly better than humans at generating ungrammatical distractors and marginally better than humans at generating nonsensical distractors, albeit slightly worse at generating plausible distractors. Moreover, vetting DQGen 2014s output and writing distractors only when necessary would halve the time to write them all, and produce higher quality distractors.

Metaphor Detection in Discourse

Understanding contextual information is key to detecting metaphors in discourse. Most current work aims at detecting metaphors given a single sentence, thus focusing mostly on local contextual cues within a short text. In this paper, we present a novel approach that explicitly leverages global context of a discourse to detect metaphors. In addition, we show that syntactic information such as dependency structures can help better describe local contextual information, thus improving detection results when combined. We apply our methods on a newly annotated online discussion forum, and show that our approach outperforms the state-of-the-art baselines in previous literature.