HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
Table 2: Our results on Subtask B.1: Clustering Arguments of Verbs to Frame-Specific Slots. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 535483), denotes a supervised Logistic Regression submission that does not comply to the task rules, denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team.
['[BOLD] Method  [ITALIC] Agglomerative Clustering', '[BOLD] Method  [ITALIC] Agglomerative Clustering', '[BOLD] Pu\xa0F1  [ITALIC] Agglomerative Clustering', '[BOLD] B3\xa0F1  [ITALIC] Agglomerative Clustering']
For our final submission (\faFire), we merged the frame types obtained by clustering the Word2Vec embeddings of the sentence (context, c) and verb (word, w), and the role labels obtained by clustering the vector of inbound dependencies (ID). However, we observed that the logistic regression model demonstrated better performance in Subtask B.2 than any clustering technique we tried, including our final submission (\faFire) and the baselines. But this performance was further improved by combining the results from post-competition experiments of Subtask A and Subtask B.2 (\faHourglassEnd).