Facilitating Discourse Analysis with Interactive Visualization

Contributors

Jian Zhao, Fanny Chevalier, Christopher Collins, Ravin Balakrishnan

Abstract

A discourse parser is a natural language processing system which can represent the organization of a document based on a rhetorical structure tree—one of the key data structures enabling applications such as text summarization, question answering and dialogue generation. Computational linguistics researchers currently rely on manually exploring and comparing the discourse structures to get intuitions for improving parsing algorithms. In this paper, we present DAViewer, an interactive visualization system for assisting computational linguistics researchers to explore, compare, evaluate and annotate the results of discourse parsers. An iterative user-centered design process with domain experts was conducted in the development of DAViewer. We report the results of an informal formative study of the system to better understand how the proposed visualization and interaction techniques are used in the real research environment.

Publications and Downloads

  • J. Zhao, F. Chevalier, C. Collins, and R. Balakrishnan, “Facilitating Discourse Analysis with Interactive Visualization,” IEEE Trans. on Visualization and Computer Graphics (Proc. of the IEEE Conf. on Information Visualization (InfoVis), vol. 18, iss. 12, pp. 2639-2648, 2012.
    [Bibtex] [PDF] [DOI]
    @article{ZHA2012a,
      author = {Jian Zhao and Fanny Chevalier and Christopher Collins and Ravin Balakrishnan},
      title = {Facilitating Discourse Analysis with Interactive Visualization},
      journal = {IEEE Trans. on Visualization and Computer Graphics (Proc. of the IEEE Conf. on Information Visualization (InfoVis)},
      volume = 18, 
      number = 12,
      month = dec, 
      year = 2012,
      pages = {2639 -- 2648},
      doi = {10.1109/TVCG.2012.226}
    }

slides download Download Demo

Video

Acknowledgement

Research

ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Detecting Negative Emotion for Mixed Initiative Visual Analytics

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop

PivotSlice

Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

Glidgets

TandemTable

Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

// Where the sidebar information is stored
| © Copyright vialab | Dr. Christopher Collins, Canada Research Chair in Linguistic Information Visualization |