Automatically Retrieving Explanatory Analogies from Webpages

arXiv (Cornell University), 2023

Analogy is one of the core capacities of human cognition; when faced with new situations, we often transfer prior experience from other domains. Most work on computational analogy relies heavily on complex, manually crafted input. In this work, we relax the input requirements, requiring only names of entities to be mapped. We automatically extract commonsense representations and use them to identify a mapping between the entities. Unlike previous works, our framework can handle partial analogies and suggest new entities to be added. Moreover, our method's output is easily interpretable, allowing for users to understand why a specific mapping was chosen. Experiments show that our model correctly maps 81.2% of classical 2x2 analogy problems (guess level=50%). On larger problems, it achieves 77.8% accuracy (mean guess level=13.1%). In another experiment, we show our algorithm outperforms human performance, and the automatic suggestions of new entities resemble those suggested by humans. We hope this work will advance computational analogy by paving the way to more flexible, realistic input requirements, with broader applicability.

APPLIED INFORMATICS-PROCEEDINGS-, 2001

retrieving analogies from presented problem data is an important phase of analogical reasoning, influencing many related cognitive processes. Existing models have focused on semantic similarity, but structural similarity is also a necessary requirement of any analogical comparison. We present a new technique for performing structure based analogy retrieval. This is founded upon derived attributes that explicitly encode elementary structural qualities of a domains representation. Crucially, these attributes are unrelated to the semantic content of the domain information, and encode only its structural qualities. We describe a number of derived attributes and detail the computation of the corresponding attribute values. We examine our models operation, detailing how it retrieves both semantically related and unrelated domains. We also present a comparison of our algorithms performance with existing models, using a structure rich but semantically impoverished domain.

Empirical Methods in Natural Language Processing, 2007

A lexical analogy is a pair of word-pairs that share a similar semantic relation. Lexical analogies occur frequently in text and are useful in various natural language processing tasks. In this study, we present a system that generates lexical analogies automatically from text data. Our system discovers semantically related pairs of words by using dependency relations, and applies novel machine learning algorithms to match these word-pairs to form lexical analogies. Empirical evaluation shows that our system generates valid lexical analogies with a precision of 70%, and produces quality output although not at the level of the best humangenerated lexical analogies.

Journal of Experimental and Theoretical Artificial Intelligence , 2022

In this paper, we outline a comprehensive approach to composed analogies based on the theory of conceptual spaces. Our algorithmic model understands analogy as a search procedure and builds upon the idea that analogical similarity depends on a conceptual phenomena called 'dimensional salience.' We distinguish between category-based, property-based, event-based, and part-whole analogies, and propose computationally-oriented methods for explicating them in terms of conceptual spaces.

Strategies for the Semi-Automatic Retrieval of Metaphorical Terms, 2011

This article proposes a method for the semi-automatic extraction of resemblance metaphor terms from a manually annotated corpus of marine biology texts in English and Spanish. The corpus was first searched for target domain terms as well as for lexical markers indicative of metaphors. The combination of these search strategies for metaphor extraction resulted in a set of English-Spanish term pairs. After analysing and comparing these metaphor candidates, a quantitative analysis provided comparative statistical data regarding marine biology metaphor. Finally, the metaphorical nature of marine biology terms was verified in three ways. The first verification strategy entailed an adapted version of the Metaphor Identification Procedure (Pragglejaz Group, 2007). The second involved the analysis of contextual data extracted from the corpus, and the third involved the analysis of visual images from an online marine biology database and from the Google search engine.

Full natural language understanding requires identifying and analyzing the meanings of metaphors, which are ubiquitous in both text and speech. Over the last thirty years, linguistic metaphors have been shown to be based on more general conceptual metaphors, partial semantic mappings between disparate conceptual domains. Though some achievements have been made in identifying linguistic metaphors over the last decade or so, little work has been done to date on automatically identifying conceptual metaphors. This paper describes research on identifying conceptual metaphors based on corpus data. Our method uses as little background knowledge as possible, to ease transfer to new languages and to minimize any bias introduced by the knowledge base construction process. The method relies on general heuristics for identifying linguistic metaphors and statistical clustering (guided by Wordnet) to form conceptual metaphor candidates. Human experiments show the system effectively finds meaningful conceptual metaphors.

2003

Innovative applications often occur at the juncture of radically different domains of research. This paper describes an emerging application, called the analogical thesaurus, that arises at the boundary of two very different domains, the highly applied domain of information retrieval, and the esoteric domain of lexical metaphor interpretation. This application has the potential to not just to improve the utility of conventional electronic thesauri, but to serve as an intelligent mapping component in any system that uses analogical reasoning or case-based reasoning. 1

2006

Analogy and metaphor are extremely knowledge-hungry processes, so one should question whether lightweight lexical ontologies like WordNet are sufficiently rich to support them. In this paper we argue that resources like WordNet are suited to the processing of certain kinds of lexical analogies and metaphors, for which we propose a spatially-motivated typology and a corresponding computational model. We identify two kinds of dimension that are important in lexical analogy - lexicalized (taxonomic) dimensions and ad-hoc (goal-specific) dimensions - and describe how these can be automatically identified, extracted and exploited in WordNet.

2003

Innovative applications often occur at the juncture of radically different domains of research. This paper describes an emerging application, called the analogical thesaurus, that arises at the boundary of two very different domains, the highly applied domain of information retrieval, and the esoteric domain of lexical metaphor interpretation. This application has the potential to not just to improve the utility of conventional electronic thesauri, but to serve as an intelligent mapping component in any system that uses analogical reasoning or casebased reasoning.

1996

Abstract. This paper defines and analyses a computational model of similarity which detects analogies between objects based on conceptual descriptions of them, constructed from classification, generalization relations and attributes. Analogies are detected (elaborated) by functions which measure conceptual distances between objects with respect to these semantic modelling abstractions. The model is domain independent and operational upon objects described in non uniform ways.