HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
Table 1: Our results on Subtask A: Grouping Verbs to Frame Type Clusters. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 536426), denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team.
['[BOLD] Method', '[BOLD] Method w2v[c+w]norm', '[BOLD] Pu\xa0F1  [BOLD] 76.68', '[BOLD] B3\xa0F1  [BOLD] 68.10']
Our final submission (\faFire) used agglomerative clustering of normalized vectors obtained by concatenating the context and verb vectors from the Word2Vec model. In particular, we found that the best performance is attained for Manhattan affinity and 150 clusters. During our post-competition experiments (\faHourglassEnd), we found that ELMo performed better than Word2Vec when a higher number of clusters, 235, was specified.