@inproceedings{fecd61e76c0a404c938e63c1671f429c,
title = "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.",
keywords = "Conceptualization, Sentence embedding, Text representation",
author = "Yashen Wang and Heyan Huang and Chong Feng and Qiang Zhou and Jiahui Gu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 17th International Conference on Web-Age Information Management, WAIM 2016 ; Conference date: 03-06-2016 Through 05-06-2016",
year = "2016",
doi = "10.1007/978-3-319-39937-9_30",
language = "English",
isbn = "9783319399362",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "390--401",
editor = "Jianliang Xu and Nan Zhang and Dexi Liu and Bin Cui and Xiang Lian",
booktitle = "Web-Age Information Management - 17th International Conference, WAIM 2016, Proceedings",
address = "Germany",
}