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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/Q13-1029.pdf
- Data
- SemEval-2010 Task-8