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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Goal of first paper was to extract narrative event chains using unsupervised methods [[AddressesProblem::extract narrative event chains using unsupervised methods]] . Whereas, goal of second paper is to find the relation between verbs [[AddressesProblem::find the relation between verbs]]. Relations considered in second paper are similarity, strength, antonymy, enablement, and temporal relations. | Goal of first paper was to extract narrative event chains using unsupervised methods [[AddressesProblem::extract narrative event chains using unsupervised methods]] . Whereas, goal of second paper is to find the relation between verbs [[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:: | + | 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 1 uses a [[UsesDataset::Gigaword corpus]]. Whereas, in Paper 2 Google is used to find number of hits for relation verb pair <math> V1,V2 </math> and surface pattern <math> P </math> from WWW[[UsesDataset::WWW]. | Paper 1 uses a [[UsesDataset::Gigaword corpus]]. Whereas, in Paper 2 Google is used to find number of hits for relation verb pair <math> V1,V2 </math> and surface pattern <math> P </math> from WWW[[UsesDataset::WWW]. | ||
Revision as of 05:32, 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 extract narrative event chains using unsupervised methods . Whereas, goal of second paper is to find the relation between verbs 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[[UsesDataset::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?
5. Was there a study plan for the old paper?
