ANP-W2V: Effects of Composition Methods for Embedding Adjective-Noun Pairs

Dayananda Herurkar, Philipp Blandfort, Federico Raue, Jörn Hees, Andreas Dengel

Abstract: Adjective-Noun Pairs (ANPs) are often used for affective computing in textual and visual domains. Due to the training cost of more current models (e.g., transformers) many approaches still rely on Word2Vec to compute ANP embeddings by combining individual word embeddings. However, when combining an adjective and a noun into an ANP, there can potentially be more complex interactions which cannot be accounted for by using Word2Vec alone. To solve these challenges, we propose ANP- W2V, an approach that puts adjectives and nouns in different embedding spaces and hence outperforms the baselines based on Word2Vec. In this paper, we do a comprehensive comparison, where we systematically evaluate the role of six different fusion methods in four different tasks with different embedding sizes. The chosen tasks not only gauge the external and internal relationships in the ANPs but can also check whether ANP embeddings capture more complex interactions between adjectives and nouns.