Word Confusion measures semantic similarity as classifier confusion between contextual embeddings, matching human judgments as well as or better than cosine similarity, and enables analyst-chosen feature dimensions.
BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representations
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity. These differences are important for natural language understanding and reasoning. We propose a novel BERT-based approach to intensity detection for scalar adjectives. We model intensity by vectors directly derived from contextualised representations and show they can successfully rank scalar adjectives. We evaluate our models both intrinsically, on gold standard datasets, and on an Indirect Question Answering task. Our results demonstrate that BERT encodes rich knowledge about the semantics of scalar adjectives, and is able to provide better quality intensity rankings than static embeddings and previous models with access to dedicated resources.
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Rethinking Word Similarity: Semantic Similarity through Classification Confusion
Word Confusion measures semantic similarity as classifier confusion between contextual embeddings, matching human judgments as well as or better than cosine similarity, and enables analyst-chosen feature dimensions.