@inproceedings{li-etal-2018-dimsim,
    title = "{DIMSIM}: An Accurate {C}hinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding",
    author = "Li, Min  and
      Danilevsky, Marina  and
      Noeman, Sara  and
      Li, Yunyao",
    editor = "Korhonen, Anna  and
      Titov, Ivan",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/K18-1043/",
    doi = "10.18653/v1/K18-1043",
    pages = "444--453",
    abstract = "Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks. However, existing approaches are designed mainly for Indo-European languages and fail to capture the unique properties of Chinese pronunciation. In this paper, we propose a high dimensional encoded phonetic similarity algorithm for Chinese, DIMSIM. The encodings are learned from annotated data to separately map initial and final phonemes into n-dimensional coordinates. Pinyin phonetic similarities are then calculated by aggregating the similarities of initial, final and tone. DIMSIM demonstrates a 7.5X improvement on mean reciprocal rank over the state-of-the-art phonetic similarity approaches."
}