Joint Detection and Location of English Puns
Table 1: Comparison results on two benchmark datasets. (P.: Precision, R.: Recall, F1: F1 score.)
['System', 'Homographic Detection', 'Homographic Detection', 'Homographic Detection', 'Homographic Location', 'Homographic Location', 'Homographic Location', 'Heterographic Detection', 'Heterographic Detection', 'Heterographic Detection', 'Heterographic Location', 'Heterographic Location', 'Heterographic Location']
For the pun detection task, if the predicted tag sequence contains at least one P tag, we regard the output (i.e., the prediction of our pun detection model) for this task as true, otherwise false. For the pun location task, a predicted pun is regarded as correct if and only if it is labeled as the gold pun in the dataset. As to pun location, to make fair comparisons with prior studies, we only consider the instances that are labeled as the ones containing a pun. Additionally, we conduct experiments over sentences containing a pun only, namely 1,607 and 1,271 instances from homographic and heterographic pun corpora separately. It can be regarded as a “pipeline” method where the classifier for pun detection is regarded as perfect. , we apply 10-fold cross validation. Compared with our approaches, the performance of such an approach drops significantly. On the other hand, such a fact demonstrates that the two task, detection and location of puns, can reinforce each other. These figures demonstrate the effectiveness of our sequence labeling method to detect and locate English puns in a joint manner.