Information Retrieval – and projects we have done.

Information Retrieval –Information Retrieval –and projects we have done.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

MotivationMotivationWeb, 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.

Information RetrievalInformation Retrieval

ApplicationsApplicationsWeb search – Google, YahooQuerying/QA system like Watson

(developed by IBM).Spam filteringAutomatic SummarizationCross-lingual retrieval

en.wikipedia.org/wiki/Information_retrieval_applications

Information RetrievalInformation RetrievalIR 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

en.wikipedia.org/wiki/Information_Retrieval

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.

9http://en.wikipedia.org/wiki/Mind_maps

Mind mapMind map

10http://www.spicynodes.org/blog/2010/05/21/stuff-we-like-climate-change-mind-maps/

What’s the difficult part?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 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.

13http://nlp.stanford.edu/software/tregex.shtml

Extraction of relevant Extraction of relevant informationinformationIdentifying 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


Extraction of relevant Extraction of relevant informationinformationPresence of a cardinal number in

a noun phrase


Extraction of relevant Extraction of relevant informationinformation

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 Extraction of relevant informationinformation

http://nlp.stanford.edu/software/tregex.shtml

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Code Generated Mind MapCode Generated Mind Map

EvaluationEvaluation

19http://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 3.23

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.24

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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Vision Based Attribute Vision Based Attribute Segmentation from lists in Segmentation from lists in Web PagesWeb Pages

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-by Rohit Kumar Saraf

Information Retrieval – and projects we have done.

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