wiki mind mapping
TRANSCRIPT
Wiki Mind MappingWiki Mind Mapping
Harshit Mittal (IIT-B)[email protected]
Aditya Tiwari (IIT-B)[email protected]
Akhil Bhiwal (VIT University)[email protected]
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Project IdeaProject 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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Research GoalResearch Goal
Use of phrases to represent semantic information.
Hierarchical representation of information of a given text
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Mind mapsMind mapsA 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.
4http://en.wikipedia.org/wiki/Mind_maps
Mind mapMind map
5http://www.spicynodes.org/blog/2010/05/21/stuff-we-like-climate-change-mind-maps/
WhatWhat’s the difficult part?’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 rules of grammar don’t apply here.
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Possible SolutionPossible 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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How we did it.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.
8http://nlp.stanford.edu/software/tregex.shtml
Extraction of relevant informationExtraction of relevant informationIdentifying 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
9http://nlp.stanford.edu/software/tregex.shtml
Extraction of relevant informationExtraction of relevant informationPresence of a cardinal number in a noun
phrase
10http://nlp.stanford.edu/software/tregex.shtml
Extraction of relevant informationExtraction 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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Ex : The name India is derived from Indus.
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Extraction of relevant informationExtraction of relevant information
http://nlp.stanford.edu/software/tregex.shtml
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Code Generated Mind MapCode Generated Mind Map
EvaluationEvaluation
14http://en.wikipedia.org/wiki/Precision_and_recall
EvaluationEvaluationSurvey 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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ObservationsObservationsPros:◦ 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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ObservationsObservationsCons◦ 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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LimitationsLimitationsBag of words and Tregex expressions is
hand-coded instead of machine learned.
Garbage phrases are being generated.
Level of hierarchy is limited to 3.
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Future workFuture workUsing 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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ReferencesReferences
http://en.wikipedia.org/wiki/Mind_mapshttp://en.wikipedia.org/wiki/Precision_and_recallTool : Stanford Parser and Stanford Tregex Match
http://nlp.stanford.edu/software/tregex.shtml
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