Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase
By Peter D. Turney. 2013
90 citations recorded by the source corpus.
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).
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Ashish Sabharwal
- Tushar Khot
- Peter Clark
- Daniel Khashabi
- Mohit Bansal
- Hannaneh Hajishirzi
- Yoav Goldberg
- Yejin Choi
Citations in the corpus
- Question Answering via Integer Programming over Semi-Structured Knowledge (2016, methodology): “It was collected by Charles Clarke at the University of Water-loo, and has been used previously by Turney [ 2013 ] and Clark et al. [ 2016 ] .”
- Linguistic Regularities in Sparse and Explicit Word Representations (2014, methodology): “…information, by either relying on a ( word - pair, connectives ) matrix rather than the standard ( word, context ) matrix (Turney and Littman, 2005; Turney, 2006), or by treating analogy detection as a supervised learning task (Ba-roni and Lenci, 2009; Jurgens et al., 2012; Turney, 2013).”
- KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge (2015, unknown): “… of text mining, information retrieval, and natural language processing tasks [Collobert and Weston 2008; Glorot et al. 2011; Mikolov et al. 2013a, 2013b; Socher et al. 2011; Turney 2013; Turney and Pantel 2010; Deng et al. 2013; Collobert et al. 2011; Mnih and Hinton 2008; Turian et al. 2010].”
- Co-learning of Word Representations and Morpheme Representations (2014, unknown): “…in a number of NLP tasks (Col-lobert and Weston, 2008) (Glorot et al., 2011) (Mikolov et al., 2013a) (Mikolov et al., 2013b) (Socher et al., 2011) (Turney, 2013) (Turney and Pantel, 2010) (Weston et al., ) (Deng et al., 2013) (Collobert et al., 2011) (Mnih and Hinton, 2008) (Turian et al., 2010).”
- Parallelizing Word2Vec in Shared and Distributed Memory (2016, background): “This idea has been applied to a wide range of NLP tasks with considerable success [4], [5], [6].”
- Exploring Numeracy in Word Embeddings (2019, background): “They have been a driving force in NLP in recent years, and enjoy widespread use in a variety of semantic tasks (Rumelhart et al.; Mikolov et al., 2013a,b; Col-lobert and Weston, 2008; Glorot et al., 2011; Tur-ney and Pantel, 2010; Turney, 2013).”