Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase

Peter D. Turney


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).
Anthology ID:
Q13-1029
Volume:
Transactions of the Association for Computational Linguistics, Volume 1
Month:
Year:
2013
Address:
Cambridge, MA
Editors:
Dekang Lin, Michael Collins
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
353–366
Language:
URL:
https://aclanthology.org/Q13-1029
DOI:
10.1162/tacl_a_00233
Bibkey:
Cite (ACL):
Peter D. Turney. 2013. Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase. Transactions of the Association for Computational Linguistics, 1:353–366.
Cite (Informal):
Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase (Turney, TACL 2013)
Copy Citation:
PDF:
https://aclanthology.org/Q13-1029.pdf
Data
SemEval-2010 Task-8