The (too Many) Problems of Analogical Reasoning with Word Vectors

By Anna Rogers, Aleksandr Drozd, Bofang Li. 2017

88 citations recorded by the source corpus.

This paper explores the possibilities of analogical reasoning with vector space models. Given two pairs of words with the same relation (e.g. man:woman :: king:queen), it was proposed that the offset between one pair of the corresponding word vectors can be used to identify the unknown member of the other pair (king - man + woman = queen). We argue against such “linguistic regularities” as a model for linguistic relations in vector space models and as a benchmark, and we show that the vector offset (as well as two other, better-performing methods) suffers from dependence on vector similarity.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor (2019, background): “It has been argued that what is observed through the analogy task might be mainly due to irrelevant neighborhood structure rather than to the vector offset that supposedly captures the analogy itself (Linzen 2016; Rogers, Drozd, and Li 2017).”
Inducing Relational Knowledge from BERT (2019, background): “…somewhat supported by theoretical insights (Arora et al. 2016; Allen and Hospedales 2019), simple linear transformations of word vectors have been found limiting in more general settings (Levy and Goldberg 2014; Linzen 2016; Rogers, Drozd, and Li 2017; Nissim, van Noord, and van der Goot 2019).”
Metrology for AI: From Benchmarks to Instruments (2019, background): “…data in AI benchmarks is collected(Son 2018; Inel 2017; 2019) • results from the evaluations of AI systems against these benchmarks are presented(Rogers, Drozd, and Li 2017; Gladkova, Drozd, and Matsuoka 2016; Wendlandt, Kummerfeld, and Mihalcea 2018; Ramakrishnan et al. 2018) According to…”
Evaluating word embedding models: methods and experimental results (2019, background): “re summation. That is, small differences are enlarged while large ones are suppressed. Therefore, it 5 is observed that the 3CosMul method offers better balance in different aspects. It was stated in [35] that many models score under 30% on analogy tests, suggesting that not all relations can be ide”