This document describes a project to represent textual information from documents in a graphical mind map format. Key points include using phrases to represent semantic information hierarchically, extracting relevant phrases from documents using heuristics and tools like the Stanford Parser and Tregex, and generating a mind map from the extracted phrases. The approach was evaluated using precision and recall metrics in a survey where participants answered questions about documents using the mind maps. The mind maps were able to extract information accurately but had low recall and some phrases needed refinement. Future work could focus on machine learning to select important keywords and patterns for phrase extraction.

1. Wiki Mind Mapping
Harshit Mittal (IIT-B)
h.mittal83@gmail.com
Aditya Tiwari (IIT-B)
adi.tiwari27@gmail.com
Akhil Bhiwal (VIT University)
bhiwalakhil@gmail.com
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2. Project Idea
Represent
the textual information in
graphical form which is easier to
understand and more intuitive to read. The
visual representation should be able to
summarize the text.
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3. Research Goal
Use
of phrases to represent semantic
information.
Hierarchical
representation of
information of a given text
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4. Mind maps
A mind
map is
a diagram used
to
represent words, ideas, tasks, or other items
linked to and arranged around a central key
word or idea.
Example Mind map in the next slide.
http://en.wikipedia.org/wiki/Mind_maps
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5. Mind map
http://www.spicynodes.org/blog/2010/05/21/stuff-we-like-climate-change-mind-maps/
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6. What’s the difficult part?
We
can’t represent information from any
article in mind-map as it is. That would
make it incoherent and clumsy.
Phrase
extraction
General
here.
rules of grammar don’t apply
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7. Possible Solution
Develop
new linguistic rules for
representation of text in visual form.
Use
existing summarization tools to
generate summary and try to represent
that in mind-map.
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8. How we did it.
Pulling
out the article section wise from the
Wikipedia page.
Parsing
each section sentence wise using the
Stanford parser.
Extracting
“relevant” phrases using Tregex
(another Stanford tool).
Putting
these phrases into a mind map,
section wise.
http://nlp.stanford.edu/software/tregex.shtml
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9. Extraction of relevant information
Identifying
subtrees from the parse tree of a
sentence that are important.
This
was done using a few heuristics like:
◦ Presence of a superlative adjective in a noun phrase
http://nlp.stanford.edu/software/tregex.shtml
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10. Extraction of relevant information
Presence
of a cardinal number in a noun
phrase
http://nlp.stanford.edu/software/tregex.shtml
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11. Extraction of relevant information
 Matching
of a particular verb to the bag of verbs
that were considered relevant for a particular
article. For example : for the history section, verbs
like find , discover, settle, decline were considered
“more useful”, as compared to words like derive,
deduce etc. which were considered useful for some
other section.
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12. Extraction of relevant information
Ex : The name India is derived from Indus.
http://nlp.stanford.edu/software/tregex.shtml
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13. Code Generated Mind Map
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14. Evaluation
http://en.wikipedia.org/wiki/Precision_and_recall
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15. Evaluation
Survey
based:
Asking
a person to generate 10 questions
from given article.
Asking
another person to answer those
question with the help of mind-map.
Repeating
the same exercise in reverse
manner for another article.
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16. Observations
Pros:
◦ Extraction of right information with high
accuracy.
◦ Concept of phrase extraction works well.
◦ High precision value were obtained (between
0.5-0.75).
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17. Observations
Cons
◦ Information presented in mindmap of low depth
is clumsy.
◦ Low recall value (0.2 – 0.4)
◦ Linking of node phrases with their apt
description.
◦ Heuristics defining “important phrases” need to
be refined.
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18. Limitations
Bag
of words and Tregex expressions is
hand-coded instead of machine learned.
Garbage
Level
phrases are being generated.
of hierarchy is limited to 3.
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19. Future work
Using
machine learning to determine the
important keywords for a given sentence.
We
want to explore the possibility of
finding patterns in subtree expressions
using machine learned approach.
Refinement
of generated phrases.
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20. References
http://en.wikipedia.org/wiki/Mind_maps
http://en.wikipedia.org/wiki/Precision_and_recall
Tool
: Stanford Parser and Stanford Tregex Match
http://nlp.stanford.edu/software/tregex.shtml
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