Comparing Unsupervised Learning of Narrative Event Chains and Mining the web for fine-grained semantic verb relations

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 Principal Component Analysis 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 $V1,V2$ and surface pattern $P$ 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?