Recognizing Humor Without Recognizing Meaning

Abstract. We present a machine learning approach for classifying sentences as one-liner jokes or normal sentences. We use no deep analysis of the meaning to try to see if it is humorous, instead we rely on a combination of simple features to see if these are enough to detect humor. Features such as word overlap with other jokes, presence of words common in jokes, ambiguity and word overlap with common idioms turn out to be useful. When training and testing on equal amounts of jokes and sentences from the British National Corpus, a classification accuracy of 85% is achieved.

• Type: Conference
• Year: 2007
• Where: WILF
• Authors: Jonas Sjöbergh, Kenji Araki
• Keywords: Artificial Intelligence, Machine Learning Approach, One-liner Jokes, WILF 2007, Word Overlap