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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2. VerbOcean:Mining the Web for Fine-Grained Semantic Verb Relations, by T. Chklovski, P. Pantel. [http://malt.ml.cmu.edu/mw/index.php/Chklovski_and_Pantel_(2004)_Verbocean:Mining_the_web_for_fine-grained_semantic_verb_relations] | 2. VerbOcean:Mining the Web for Fine-Grained Semantic Verb Relations, by T. Chklovski, P. Pantel. [http://malt.ml.cmu.edu/mw/index.php/Chklovski_and_Pantel_(2004)_Verbocean:Mining_the_web_for_fine-grained_semantic_verb_relations] | ||
| − | == Problem == | + | == Problem, method and dataset == |
Goal of first paper is 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. | Goal of first paper is 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. | ||
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Paper 2 uses approach motivated by mutual information to compute strength of 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 2 uses approach motivated by mutual information to compute strength of 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. | ||
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Paper 1 uses a 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>. | Paper 1 uses a 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>. | ||
Revision as of 00:59, 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 is 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 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 .
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?
