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Duygu Simsek, Simon Buckingham Shum, Anna De Liddo, Rebecca Ferguson — The Open University, UK Ágnes Sándor — Xerox Research Centre Europe, FR
1st International Workshop on Discourse-Centric Learning Analytics April 8, 2013, LAK13 Conference, Leuven, Belgium
XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of Scientific Metadiscourse
Metadiscourse
Xerox Incremental Parser
Visual analytics v0.1: XIP Dashboard
User Scenarios & Evaluation
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Metadiscourse signals important moves in educated/scholarly narrative
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(When scholarly culture works well) this is what gets your papers accepted by
reviewers, and quoted by others
Clear statements regarding the problem, the claim, the argument, the evidence, the implications…
This is what we teach students from school
upwards
Rhetorical functions of metadiscourse identified by the Xerox Incremental Parser (XIP)
BACKGROUND KNOWLEDGE:
Recent studies indicate …
… the previously proposed …
… is universally accepted ...
NOVELTY:
... new insights provide direct evidence ...
... we suggest a new ... approach ...
... results define a novel role ...
OPEN QUESTION: … little is known … … role … has been elusive
Current data is insufficient …
GENERALIZING: ... emerging as a promising approach Our understanding ... has grown exponentially ... ... growing recognition of the
importance ...
CONTRASTING IDEAS: … unorthodox view resolves … paradoxes …
In contrast with previous hypotheses ...
... inconsistent with past findings ...
SIGNIFICANCE: studies ... have provided important advances
Knowledge ... is crucial for ... understanding
valuable information ... from studies
SURPRISE: We have recently observed ... surprisingly
We have identified ... unusual The recent discovery ... suggests intriguing roles
SUMMARIZING: The goal of this study ... Here, we show ...
Altogether, our results ... indicate
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Xerox Incremental Parser (XIP)
Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search. Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.
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Xerox Incremental Parser (XIP)
Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search. Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.
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Xerox Incremental Parser (XIP)
Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search. Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.
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Xerox Incremental Parser (XIP)
Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search. Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.
Initial evaluation of XIP is promising, but methodologically complex
Human analyst XIP
A striking example – but not all were like this (De Liddo et al, 2012)
19 sentences annotated 22 sentences annotated 11 sentences same as human annotation
71 sentences annotated 59 sentences annotated 42 sentences same as human annotation
Document 1
Document 2
Extract from annotation comparison:
Xerox Incremental Parser (XIP)
XIP’s raw output is fine for NLP machines/researchers, but
not learner/educator friendly
Xerox Incremental Parser (XIP)
XIP’s raw output is fine for NLP machines/researchers, but
not learner/educator friendly
Xerox Incremental Parser (XIP)
5000 (or even 30) plain text files…
we need overviews of XIP analyses from
a corpus
Making XIP analytics visible: 1. annotations on the full text using the OU’s Cohere social sensemaking app (Firefox add-on)
Making XIP analytics visible: 2. XIP annotations visualized in Cohere as a network around the document
?
? ?
?
2nd phase analysis of document-concept clouds… Connecting? Merging? Re-tagging? Summarising?
Making XIP analytics visible (2)
XIP Dashboard: towards an earlier phase dashboard for navigating XIP output
Draw attention to patterns of potential significance to students, educators and experienced researchers alike:
§ the occurrence of domain concepts in different metadiscourse contexts – e.g. effective tutoring dialogue in sentences classified contrast
§ trends of the above over time, e.g. to show the development of an idea
§ trends within and differences between research communities as reflected in their publications
§ eventually, the above for one’s own writing 16
Paper prototype to elicit initial reactions
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Paper prototype to elicit initial reactions
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‘Intro movie’ from researcher
Participants point + click with finger
Basic navigation seems fine
Enthusiasm for a tool that could help with literature
analysis
Also for a tool to improve one’s own writing by showing
trends, or inconsistencies
XIP Dashboard Temporal trends per corpus
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Similar patterns for LAK & EDM literatures
Summary & Contrast categories relatively
higher, and rising
(Not controlled for different corpus sizes in
these graphs)
XIP Dashboard Comparing corpora filtered by concept
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XIP Dashboard All papers by year and concept, with colour = concept density (v2 mockup)
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XIP Dashboard Rhetorical function of the sentences behind each bubble
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XIP Dashboard Heatmap of all concepts by rhetorical classification (v2 mockup)
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XIP Dashboard User scenarios… Student / Educator / Researcher
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Familiarization with the background material in
a literature…
Comparing different writing patterns between
communities, or students…
Focusing on specific concepts of interest in
combination with rhetorical context
XIP Dashboard User Evaluations
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Signal-noise ratio?
Deeper or shallower reading?
New insights, or just faster insights?
Better writing, or just gaming the system?
Summary Early phases of work: a promising language technology now has visual analytics we can deploy with stakeholders
Beyond number / size / frequency of posts; ‘hottest thread’
An important feature of educated writing is knowing how to signal substantive rhetorical moves. NLP can detect this, and we can now generate rudimentary visual analytics.
To be continued…
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