@article{turney-2013-distributional,
    title = "Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase",
    author = "Turney, Peter D.",
    editor = "Lin, Dekang  and
      Collins, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "1",
    year = "2013",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest_wac_2008/Q13-1029/",
    doi = "10.1162/tacl_a_00233",
    pages = "353--366",
    abstract = "There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval 2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions)."
}