Experiments in Learning to Solve Formal Analogical Equations

Rhouma, Rafik; Langlais, Philippe

doi:10.1007/978-3-030-01081-2_40

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It departs from case-based reasoning [29]. Beyond different classical works on analogical reasoning such as [15,35,14,13,16]), there has been a noticeable renewal of interest in analogical studies with a variety of approaches, ranging from reasoning [2], machine learning [23,5] to word analogies [6,11,36,37,20,27] and natural language processing [19,12,34]. These approaches have in common to deal with analogical proportions, i.e., statements of the form "a is to b as c is to d" relating 4 items a, b, c and d [30].…”

Section: Introductionmentioning

confidence: 99%

Analogies Between Sentences: Theoretical Aspects - Preliminary Experiments

Afantenos

Kunze

Lim

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Analogical proportions hold between 4 items a, b, c, d insofar as we can consider that "a is to b as c is to d". Such proportions are supposed to obey postulates, from which one can derive Boolean or numerical models that relate vector-based representations of items making a proportion. One basic postulate is the preservation of the proportion by permuting the central elements b and c. However this postulate becomes debatable in many cases when items are words or sentences. This paper proposes a weaker set of postulates based on internal reversal, from which new Boolean and numerical models are derived. The new system of postulates is used to extend a finite set of examples in a machine learning perspective. By embedding a whole sentence into a real-valued vector space, we tested the potential of these weaker postulates for classifying analogical sentences into valid and non-valid proportions. It is advocated that identifying analogical proportions between sentences may be of interest especially for checking discourse coherence, question-answering, argumentation and computational creativity. The proposed theoretical setting backed with promising preliminary experimental results also suggests the possibility of crossing a real-valued embedding with an ontology-based representation of words. This hybrid approach might provide some insights to automatically extract analogical proportions in natural language corpora.

show abstract