Difference between revisions of "Comparing Unsupervised Learning of Narrative Event Chains and Mining the web for fine-grained semantic verb relations"
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== Problem, method and dataset == | == Problem, method and dataset == | ||
| − | Goal of first paper was to | + | Goal of first paper was to [[AddressesProblem::extract narrative event chains using unsupervised methods]] . Whereas, goal of second paper is to [[AddressesProblem::find the relation between verbs]]. Relations considered in second paper are similarity, strength, antonymy, enablement, and temporal relations. |
Paper 2 uses approach motivated by mutual information to compute strength of association/relation between verb pairs and surface pattern. Paper 1 uses mutual information motivated approach to induce narrative events. It uses [[UsesMethod::Support Vector Machines]] for temporal ordering of connected events. | Paper 2 uses approach motivated by mutual information to compute strength of association/relation between verb pairs and surface pattern. Paper 1 uses mutual information motivated approach to induce narrative events. It uses [[UsesMethod::Support Vector Machines]] for temporal ordering of connected events. | ||
Revision as of 05:34, 6 November 2012
Papers
1. Unsupervised Learning of Narrative Event Chains, by N. Chambers, D. Jurafsky. [1]
2. VerbOcean:Mining the Web for Fine-Grained Semantic Verb Relations, by T. Chklovski, P. Pantel. [2]
Problem, method and dataset
Goal of first paper was to extract narrative event chains using unsupervised methods . Whereas, goal of second paper is to find the relation between verbs. Relations considered in second paper are similarity, strength, antonymy, enablement, and temporal relations.
Paper 2 uses approach motivated by mutual information to compute strength of association/relation between verb pairs and surface pattern. Paper 1 uses mutual information motivated approach to induce narrative events. It uses Support Vector Machines for temporal ordering of connected events.
Paper 1 uses a Gigaword corpus. Whereas, in Paper 2 Google is used to find number of hits for relation verb pair and surface pattern from WWW.
Additional Questions
1. How much time did you spend reading the (new, non-wikified) paper you summarized? 2 hour
2. How much time did you spend reading the old wikified paper? 1 hours
3. How much time did you spend reading the summary of the old paper? 10 minutes
4. How much time did you spend reading background materiel? Not Much.
5. Was there a study plan for the old paper? No,
