@article{sultan-etal-2014-back,
    title = "Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence",
    author = "Sultan, Md Arafat  and
      Bethard, Steven  and
      Sumner, Tamara",
    editor = "Lin, Dekang  and
      Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "2",
    year = "2014",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/Q14-1018/",
    doi = "10.1162/tacl_a_00178",
    pages = "219--230",
    abstract = "We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88{--}92{\%}, demonstrating 1{--}3{\%} absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task."
}