Conceptual sentence embeddings

Abstract

Most sentence embedding models typically represent each sentence only using word surface, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ concept conceptualization model to assign associated concepts for each sentence in the text corpus, and learn conceptual sentence embedding (CSE). Hence, the sentence representations are more expressive than some widely-used document representation models such as latent topic models, especially for short text. In the experiments, we evaluate the CSE models on two tasks, text classification and information retrieval. The experimental results show that the proposed models outperform typical sentence embedding models.