Difference between revisions of "User:Manajs"

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Manaj Srivastava
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<b>Manaj Srivastava</b>
  
 
http://www.cs.cmu.edu/~manajs/
 
http://www.cs.cmu.edu/~manajs/
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I am a second year Masters student in the LTI. I have taken this course because the syllabus matter aligns pretty well with my current research on Information Extraction.
 
I am a second year Masters student in the LTI. I have taken this course because the syllabus matter aligns pretty well with my current research on Information Extraction.
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Click [[Information Extraction to Predict Decisions|here]] to go to the project page.
  
 
[[Analysis of Social Media Spring, 2011|Link]] to my wiki page for the "Analysis of Social Media" course taken in Spring, 2011.
 
[[Analysis of Social Media Spring, 2011|Link]] to my wiki page for the "Analysis of Social Media" course taken in Spring, 2011.
  
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==Idea for the Project==
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'''Wiki Writeups'''
'''Relevant Information Extraction from Court-room Hearings To Predict the Decision'''
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[[Semantic Role Labeling with CRF|Cohn and Blunsom: Semantic Role Labeling with Tree Conditional Random Fields - Paper (due 10/1)]]
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[[Semantic Role Labeling as Sequential Tagging|Marques et. al.: Semantic Role Labeling as Sequential Tagging - Paper (due 10/1)]]
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[[Learning Domain-Specific Information Extraction Patterns from the Web|Patwardhan and Riloff: Learning Domain-Specific Information Extraction Patterns from the Web - Paper (due 10/1)]]
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[[Semantic Affinity|Semantic Affinity - Method (due 10/1)]]
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[[Unsupervised Modeling of Dialog Acts in Asynchronous Conversation | Joty et. al.: Unsupervised Modeling of Dialog Acts in Asynchronous Conversation - Paper (due 11/2)]]
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[[Modeling of Stylistic Variation in Social Media with Stretchy Patterns | Gianfortoni et. al.: Modeling of Stylistic Variation in Social Media with Stretchy Patterns - Paper (due 11/2)]]
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[[Joint Inference in Information Extraction | Poon and Domingos: Joint Inference in Information Extraction - Paper (due 12/1)]]
  
The bigger idea is to analyze conversational speech transcripts of court-room hearings, and extract relevant information that impacts the decision of the hearings. A possible approach is to first identify the bases of making decision on a case from relevant law (e.g. objective of the crime, manner of the crime, etc.), and then do some supervised/semi-supervised learning to identify from the conversation, portions that relate to discussion regarding these bases, and to find whether that portion of the conversation tends to work in the favor of the accused or otherwise.
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[[Automatic Detection and Classification of Social Events | Agarwal and Rambow: Automatic Detection and Classification of Social Events  - Paper (due 12/1)]]

Latest revision as of 06:34, 7 December 2011