waashk/dblp huggingface dataset:
can we all stand under our umbrella: the arts and design research in hci. the arts (i.e., all liberal, cultural, literary, visual and performing arts disciplines) are becoming more prominent at chi. this sig will take stock of what they can contribute, and how and why, and what the chi community needs to do to more fully embrace the arts to advance the leading edge of design research.
pixsmix: visual ambiguity as a means of designing interpersonal connection. strategies for meeting people online are often based on appearance or demographics, criteria that do not guarantee quality connections or long-lasting relationships. drawing from prior work in ambiguity and affective interaction, pixsmix is a conceptual design to facilitate human connection through visual expression and interpretation. participants create mosaics formed from a dozen public images, co-creating meaning with those who view and interact with the social artifact. to explore the validity and dynamics of this process, we gathered feedback using a paper prototype and a task-oriented focus group. the early outcomes support the notion of ambiguous design as an engaging creative activity and, through sharing of new social artifacts, as rewarding reflective experience.
supporting effective user navigation in digital documents. electronic documents such as pdfs are becoming increasingly popular as we move further towards the notion of the paperless office. the harsh truth however is that e-documents differ greatly from their physical paper counterparts, with many users opting to print them before reading. this paper describes several novel implementations that utilize a technique known as 'lightweight interaction'; a term that describes activities that can be performed without excessive cognitive attention. incorporating tools into digital document readers to aid users in day-to-day tasks will enhance their performance and hopefully increase user uptake of digital reading. my research on this topic centers on several areas of document navigation, focusing specifically on current physical (paper) practices, in order to enhance their digital equivalents.
a survey to assess the potential of mobile phones as a learning platform for panama. education is a major concern in developing countries. we believe that new and emerging technologies offer hope in improving their educational systems. while the use of personal computers in developing countries is still very low, they have seen a widespread adoption of mobile phones in recent years. since mobile phones have become small computing platforms, this inspired us to investigate their potential as educational tools. in this paper we report on a large survey (300 school children, 85 teachers) that was carried out in panama to assess the status quo of technology use, as well as the initial ideas of the potential of using mobile phones in the context of school education. results show that there is a high proliferation of mobile phones among school children, and that teachers and pupils were all able to envision using mobile phones for learning purposes. the results indicate that mobile devices have the potential to integrate into existing learning contexts, as well as enable new learning contexts.
critical gameplay: software studies in computer gameplay. the computer game software with which we interact on a daily basis not only entertains us, it trains us into specific patterns. critical gameplay is a design practice which endeavors to expose and redesign the patterns to which standard gameplay subscribes. the ongoing project seeks to identify the dominant values, philosophies and problem solving models reinforced by computer games and provides prototypical alternates to those standards.
remote skincare advice system using life logs. many women find it difficult to maintain beautiful skin since different skincare approaches require different amounts of effort, time, and special knowledge. women often ask experts in cosmetic stores for skincare advice. however, this approach has the limitations of time, place, and personal information. to solve these problems, we propose a remote skincare advice system that uses life logs. this system helps users to automatically log information related to their skin condition and share these data with skincare experts in order to obtain appropriate advice.
models, theories and methods of studying online behaviour. while there is a growing body of work that documents online behavior in its different forms, there has been little research that develops holistic models and theories of online behavior. this workshop will draw together internet researchers to develop new understandings of online behavior across a diversity of activities and applications. the emphasis is on new theories and models that can be used to understand and predict social behavior as underlying technologies change. this workshop will work as a valuable bridge across individual disciplines and empirical studies supporting the generalization of understandings and approaches.
mobigaze: development of a gaze interface for handheld mobile devices. handheld mobile devices that have a touch screen are widely used but are awkward to use with one hand. to solve this problem, we propose mobigaze, which is a user interface that uses one's gaze (gaze interface) to operate a handheld mobile device. by using stereo cameras, the user's line of sight is detected in 3d, enabling the user to interact with a mobile device by means of his/her gaze. we have constructed a prototype system of mobigaze that consists of two cameras with ir-led, a windows-based notebook pc, and ipod touch. moreover, we have developed several applications for mobigaze.
measuring environments for public displays: a space syntax approach. this paper reports on an on-going project, which is investigating the role that location plays in the visibility of information presented on a public display. spatial measures are presented, derived from the architectural theory of space syntax. these are shown to relate to the memorability of words and images presented on different displays. results show a complex pattern of interactions between the size and shape of spaces in which displays are situated and the memorability of different types of representations depicted. this approach offers a new way to consider the role of space in guiding and constraining interaction in real settings: a growing concern within hci and ubicomp.
addressing challenges in doing international field research. this panel will discuss some of the key challenges in doing international field research including issues with planning, conducting, interpreting, and reporting on such research. panelists will also share potential solutions and approaches they have used to try to deal with these challenges, and will discuss with the audience additional challenges that audience members have encountered, offering ideas on how to address these as appropriate.
encouraging awareness of peers' learning activities using large displays in the periphery. learners benefit from creating personally meaningful artifacts for an audience, especially when those artifacts embody concepts the learners aim to understand. in this pilot study, we explored ways to expand opportunities for sharing mathematical artifacts with a larger audience (beyond learners seated next to each other) by incorporating structured ways to share work on a large display. we changed the functionality of the large display throughout the experiment to explore different content management schemas. early results suggest children's awareness of their peers' work increases with the use of the large display, but that they tend to share only finished work.
an integrated probabilistic and logic approach to encyclopedia relation extraction with multiple features. we propose a new integrated approach based on markov logic networks (mlns), an effective combination of probabilistic graphical models and first-order logic for statistical relational learning, to extracting relations between entities in encyclopedic articles from wikipedia. the mlns model entity relations in a unified undirected graph collectively using multiple features, including contextual, morphological, syntactic, semantic as well as wikipedia characteristic features which can capture the essential characteristics of relation extraction task. this model makes simultaneous statistical judgments about the relations for a set of related entities. more importantly, implicit relations can also be identified easily. our experimental results showed that, this integrated probabilistic and logic model significantly outperforms the current state-of-the-art probabilistic model, conditional random fields (crfs), for relation extraction from encyclopedic articles.
linguistically annotated btg for statistical machine translation. bracketing transduction grammar (btg) is a natural choice for effective integration of desired linguistic knowledge into statistical machine translation (smt). in this paper, we propose a linguistically annotated btg (labtg) for smt. it conveys linguistic knowledge of source-side syntax structures to btg hierarchical structures through linguistic annotation. from the linguistically annotated data, we learn annotated btg rules and train linguistically motivated phrase translation model and reordering model. we also present an annotation algorithm that captures syntactic information for btg nodes. the experiments show that the labtg approach significantly outperforms a baseline btg-based system and a state-of-the-art phrase-based system on the nist mt-05 chinese-to-english translation task. moreover, we empirically demonstrate that the proposed method achieves better translation selection and phrase reordering.
from words to senses: a case study of subjectivity recognition. we determine the subjectivity of word senses. to avoid costly annotation, we evaluate how useful existing resources established in opinion mining are for this task. we show that results achieved with existing resources that are not tailored towards word sense subjectivity classification can rival results achieved with supervision on a manually annotated training set. however, results with different resources vary substantially and are dependent on the different definitions of subjectivity used in the establishment of the resources.
tracking the dynamic evolution of participants salience in a discussion. we introduce a technique for analyzing the temporal evolution of the salience of participants in a discussion. our method can dynamically track how the relative importance of speakers evolve over time using graph based techniques. speaker salience is computed based on the eigenvector centrality in a graph representation of participants in a discussion. two participants in a discussion are linked with an edge if they use similar rhetoric. the method is dynamic in the sense that the graph evolves over time to capture the evolution inherent to the participants salience. we used our method to track the salience of members of the us senate using data from the us congressional record. our analysis investigated how the salience of speakers changes over time. our results show that the scores can capture speaker centrality in topics as well as events that result in change of salience or influence among different participants.
using three way data for word sense discrimination. in this paper, an extension of a dimensionality reduction algorithm called non-negative matrix factorization is presented that combines both 'bag of words' data and syntactic data, in order to find semantic dimensions according to which both words and syntactic relations can be classified. the use of three way data allows one to determine which dimension(s) are responsible for a certain sense of a word, and adapt the corresponding feature vector accordingly, 'subtracting' one sense to discover another one. the intuition in this is that the syntactic features of the syntax-based approach can be disambiguated by the semantic dimensions found by the bag of words approach. the novel approach is embedded into clustering algorithms, to make it fully automatic. the approach is carried out for dutch, and evaluated against eurowordnet.
when is self-training effective for parsing? self-training has been shown capable of improving on state-of-the-art parser performance (mcclosky et al., 2006) despite the conventional wisdom on the matter and several studies to the contrary (charniak, 1997; steedman et al., 2003). however, it has remained unclear when and why self-training is helpful. in this paper, we test four hypotheses (namely, presence of a phase transition, impact of search errors, value of non-generative reranker features, and effects of unknown words). from these experiments, we gain a better understanding of why self-training works for parsing. since improvements from self-training are correlated with unknown bigrams and biheads but not unknown words, the benefit of self-training appears most influenced by seeing known words in new combinations.
extractive summarization using supervised and semi-supervised learning. it is difficult to identify sentence importance from a single point of view. in this paper, we propose a learning-based approach to combine various sentence features. they are categorized as surface, content, relevance and event features. surface features are related to extrinsic aspects of a sentence. content features measure a sentence based on content-conveying words. event features represent sentences by events they contained. relevance features evaluate a sentence from its relatedness with other sentences. experiments show that the combined features improved summarization performance significantly. although the evaluation results are encouraging, supervised learning approach requires much labeled data. therefore we investigate co-training by combining labeled and unlabeled data. experiments show that this semi-supervised learning approach achieves comparable performance to its supervised counterpart and saves about half of the labeling time cost.
a systematic comparison of phrase-based, hierarchical and syntax-augmented statistical mt. probabilistic synchronous context-free grammar (pscfg) translation models define weighted transduction rules that represent translation and reordering operations via nonterminal symbols. in this work, we investigate the source of the improvements in translation quality reported when using two pscfg translation models (hierarchical and syntax-augmented), when extending a state-of-the-art phrase-based baseline that serves as the lexical support for both pscfg models. we isolate the impact on translation quality for several important design decisions in each model. we perform this comparison on three nist language translation tasks; chinese-to-english, arabic-to-english and urdu-to-english, each representing unique challenges.
shift-reduce dependency dag parsing. most data-driven dependency parsing approaches assume that sentence structure is represented as trees. although trees have several desirable properties from both computational and linguistic perspectives, the structure of linguistic phenomena that goes beyond shallow syntax often cannot be fully captured by tree representations. we present a parsing approach that is nearly as simple as current data-driven transition-based dependency parsing frameworks, but outputs directed acyclic graphs (dags). we demonstrate the benefits of dag parsing in two experiments where its advantages over dependency tree parsing can be clearly observed: predicate-argument analysis of english and syntactic analysis of danish with a representation that includes long-distance dependencies and anaphoric reference links.
mind the gap: dangers of divorcing evaluations of summary content from linguistic quality. in this paper, we analyze the state of current human and automatic evaluation of topic-focused summarization in the document understanding conference main task for 2005--2007. the analyses show that while rouge has very strong correlation with responsiveness for both human and automatic summaries, there is a significant gap in responsiveness between humans and systems which is not accounted for by the rouge metrics. in addition to teasing out gaps in the current automatic evaluation, we propose a method to maximize the strength of current automatic evaluations by using the method of canonical correlation. we apply this new evaluation method, which we call rose (rouge optimal summarization evaluation), to find the optimal linear combination of rouge scores to maximize correlation with human responsiveness.
diagnostic evaluation of machine translation systems using automatically constructed linguistic check-points. we present a diagnostic evaluation platform which provides multi-factored evaluation based on automatically constructed check-points. a check-point is a linguistically motivated unit (e.g. an ambiguous word, a noun phrase, a verb~obj collocation, a prepositional phrase etc.), which are pre-defined in a linguistic taxonomy. we present a method that automatically extracts check-points from parallel sentences. by means of checkpoints, our method can monitor a mt system in translating important linguistic phenomena to provide diagnostic evaluation. the effectiveness of our approach for diagnostic evaluation is verified through experiments on various types of mt systems.
emotion classification using massive examples extracted from the web. in this paper, we propose a data-oriented method for inferring the emotion of a speaker conversing with a dialog system from the semantic content of an utterance. we first fully automatically obtain a huge collection of emotion-provoking event instances from the web. with japanese chosen as a target language, about 1.3 million emotion provoking event instances are extracted using an emotion lexicon and lexical patterns. we then decompose the emotion classification task into two sub-steps: sentiment polarity classification (coarsegrained emotion classification), and emotion classification (fine-grained emotion classification). for each subtask, the collection of emotion-proviking event instances is used as labelled examples to train a classifier. the results of our experiments indicate that our method significantly outperforms the baseline method. we also find that compared with the single-step model, which applies the emotion classifier directly to inputs, our two-step model significantly reduces sentiment polarity errors, which are considered fatal errors in real dialog applications.
on robustness and domain adaptation using svd for word sense disambiguation. in this paper we explore robustness and domain adaptation issues for word sense disambiguation (wsd) using singular value decomposition (svd) and unlabeled data. we focus on the semi-supervised domain adaptation scenario, where we train on the source corpus and test on the target corpus, and try to improve results using unlabeled data. our method yields up to 16.3% error reduction compared to state-of-the-art systems, being the first to report successful semi-supervised domain adaptation. surprisingly the improvement comes from the use of unlabeled data from the source corpus, and not from the target corpora, meaning that we get robustness rather than domain adaptation. in addition, we study the behavior of our system on the target domain.
authorship attribution and verification with many authors and limited data. most studies in statistical or machine learning based authorship attribution focus on two or a few authors. this leads to an overestimation of the importance of the features extracted from the training data and found to be discriminating for these small sets of authors. most studies also use sizes of training data that are unrealistic for situations in which stylometry is applied (e.g., forensics), and thereby overestimate the accuracy of their approach in these situations. a more realistic interpretation of the task is as an authorship verification problem that we approximate by pooling data from many different authors as negative examples. in this paper, we show, on the basis of a new corpus with 145 authors, what the effect is of many authors on feature selection and learning, and show robustness of a memory-based learning approach in doing authorship attribution and verification with many authors and limited training data when compared to eager learning methods such as svms and maximum entropy learning.
are morpho-syntactic features more predictive for the resolution of noun phrase coordination ambiguity than lexico-semantic similarity scores? coordinations in noun phrases often pose the problem that elliptified parts have to be reconstructed for proper semantic interpretation. unfortunately, the detection of coordinated heads and identification of elliptified elements notoriously lead to ambiguous reconstruction alternatives. while linguistic intuition suggests that semantic criteria might play an important, if not superior, role in disambiguating resolution alternatives, our experiments on the reannotated wsj part of the penn treebank indicate that solely morpho-syntactic criteria are more predictive than solely lexico-semantic ones. we also found that the combination of both criteria does not yield any substantial improvement.
a method for automatic pos guessing of chinese unknown words. this paper proposes a method for automatic pos (part-of-speech) guessing of chinese unknown words. it contains two models. the first model uses a machine-learning method to predict the pos of unknown words based on their internal component features. the credibility of the results of the first model is then measured. for low-credibility words, the second model is used to revise the first model's results based on the global context information of those words. the experiments show that the first model achieves 93.40% precision for all words and 86.60% for disyllabic words, which is a significant improvement over the best results reported in previous studies, which were 89% precision for all words and 74% for disyllabic words. further, the second model improves the results by 0.80% precision for all words and 1.30% for disyllabic words.
dependency-based n-gram models for general purpose sentence realisation. we present dependency-based n-gram models for general-purpose, wide-coverage, probabilistic sentence realisation. our method linearises unordered dependencies in input representations directly rather than via the application of grammar rules, as in traditional chart-based generators. the method is simple, efficient, and achieves competitive accuracy and complete coverage on standard english (penn-ii, 0.7440 bleu, 0.05 sec/sent) and chinese (ctb6, 0.7123 bleu, 0.14 sec/sent) test data.
bayesian semi-supervised chinese word segmentation for statistical machine translation. words in chinese text are not naturally separated by delimiters, which poses a challenge to standard machine translation (mt) systems. in mt, the widely used approach is to apply a chinese word segmenter trained from manually annotated data, using a fixed lexicon. such word segmentation is not necessarily optimal for translation. we propose a bayesian semi-supervised chinese word segmentation model which uses both monolingual and bilingual information to derive a segmentation suitable for mt. experiments show that our method improves a state-of-the-art mt system in a small and a large data environment.