Event Representation Learning Enhanced with External Commonsense Knowledge
Table 2: Case study of the cosine similarity score changes with incorporating the intent and sentiment. oScore is the original cosine similarity score without intent and sentiment, and mScore is the modified cosine similarity score with intent and sentiment.
['Event1 man clears test', 'Event 2 he passed exam', 'oScore -0.08', 'mScore 0.40', 'Event1 man passed car', 'Event 2 man passed exam', 'oScore 0.81', 'mScore 0.12']
For example, the original similarity score of two events “chef cooked pasta” and “chef cooked books” is very high (0.89) as they have high lexical overlap. However, their intents differ greatly. The intent of “chef cooked pasta” is “to hope his customer enjoying the delicious food”, while the intent of “chef cooked books” is “to falsify their financial statements”. Enhanced with the intents, the similarity score of the above two events dramatically drops to 0.45. For another example, as the event pair “man clears test” and “he passed exam” share the same sentiment polarity, their similarity score is boosted from -0.08 to 0.40.