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This paper develops a vector logic for formal semantics, characterizing when compressed vector representations allow exact linear or affine readouts of truth conditions, with experiments on GloVe and word2vec embeddings.
This paper investigates whether Word2Vec can generate meaningful semantic embeddings for Toki Pona, a constructed language with only ~130 words, using a corpus of 1.4 million sentences, and examines the effect of non-Toki Pona tokens on embedding quality.