Information Retrieval

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Information Retrieval and projects we have done.

• Group Members
• Aditya Tiwari (08005036)
• Harshit Mittal (08005032)
• Rohit Kumar Saraf (08005040)
• Vinay Surana (08005031)

Guided by Prof. Pushpak Bhattacharyya
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Motivation

• Web, documents and encyclopedia all have
  tremendous amount of data and information in
  them. The information thus available serves only
  the intent of the creator or collector of data.
• However, there can be other uses of that
  data/information as well. The need is to mine the
  right information from the data and use it
  appropriately.

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Information Retrieval
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Applications

• Web search Google, Yahoo
• Querying/QA system like Watson (developed by
  IBM).
• Spam filtering
• Automatic Summarization
• Cross-lingual retrieval

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Information Retrieval

• IR is the study of concerned with searching for
  documents, and for metadata about documents, as
  well as that of searching relational databases
  and the WWW.
• The data objects that are collected can be
  images, documents, videos, mind maps, music

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Wiki Mind Mapping

• Harshit Mittal (IIT-B)
• h.mittal83_at_gmail.com
• Aditya Tiwari (IIT-B)
• adi.tiwari27_at_gmail.com
• Akhil Bhiwal (VIT University)
• bhiwalakhil_at_gmail.com

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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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Research Goal

• Use of phrases to represent semantic information.
• Hierarchical representation of information of a
  given text

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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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Mind map
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Whats the difficult part?

• We cant represent information from any article
  in mind-map as it is. That would make it
  incoherent and clumsy.
• Phrase extraction
• General rules of grammar dont apply here.

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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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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.

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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

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Extraction of relevant information

• Presence of a cardinal number in a noun phrase

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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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Extraction of relevant information

• Ex The name India is derived from Indus.

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Code Generated Mind Map
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Evaluation

•  

http//en.wikipedia.org/wiki/Precision_and_recall
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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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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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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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Limitations

• Bag 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 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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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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Vision Based Attribute Segmentation from lists in
Web Pages
-by Rohit Kumar Saraf