A Post-Modern Approach to Automatic Metaphor Identification
Authors: Dario Del Fante, Federico Manzella, Guido Sciavicco, Eduard Ionel Stan (Università di Ferrara, Libera Università di Bolzano, 2023)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper provides the theoretical bases for a symbolic approach to text classification, particularly metaphor identification, that generalizes the existing ones and is inspired by similar generalizations of symbolic approaches to learning models for non-text-related tasks.

Introduction
Metaphors involve talking and, potentially, thinking of one thing in terms of another; the two things are different, but we can perceive sets of correspondences between them. In other words, a metaphor corresponds to using a word or phrase from the context in which it is expected to occur to another context, where it is not expected to occur [1]. Metaphors are ubiquitous in language [2]: they cannot only be considered a pure artistic ornament that exclusively pertains to literary discourse, but they are essential for the development of language and culture [3, 4, 5]. Much work has been devoted to discussing the metaphor identification and interpretation process, as in [6]. In this sense, a qualitative approach represents the safest methodology since metaphors regard an aspect of language that occasionally can be ambiguous. For example, two speakers from the same linguistic and cultural context can interpret the same metaphor differently. However, this approach is time-consuming and requires at least more than two human coders to be effectively reliable. Despite this phenomenon, it remains a computationally hard task given the many structural problems that make automatic identification not quickly effective [7]. Scholars between digital humanities and computational linguistics have developed different approaches to support automatic identification. Indeed, the recent improvements regarding artificial intelligence and machine learning might consistently impact metaphor research regarding the time and quantity of analyzed text [8]. From a computational point of view, metaphor identification is a particular case of text classification. The recent literature on general text classification, particularly metaphor identification, is quite broad and includes both top-down approaches [9] and bottom-up ones. Top-down approaches start from a human-designed theory of the phenomenon, which is later digitalized to provide automatic identification. Bottom-up, or data-driven ones, on the other hand, aim to perform identification starting from a dataset of examples. Bottom-up strategies can be, in turn, separated into symbolic and sub-symbolic approaches. Sub-symbolic approaches, commonly realized via several types of neural networks, produce black-box models which in some cases can be very accurate [10]. Along with the application of pre-trained and large language models they currently are a de-facto standard for text-related learning tasks, and quite a lot of results exist even in the narrow field of metaphor identification (see, among many others, [11, 10, 12, 13, 14]). Conversely, the purpose of a symbolic approach is to provide an identification model and a statistically validated theory of the phenomenon, written in a suitable logical language. While symbolic systems are sometimes used for text-related tasks in general, their application to the case of metaphor identification needs to be addressed. In this paper, we provide the theoretical bases for a symbolic approach to text classification, particularly metaphor identification, that generalizes the existing ones and is inspired by similar generalizations of symbolic approaches to learning models for non-text-related tasks.

Conclusion
This work represents an initial attempt to approach symbolic learning for text-related tasks like metaphor detection. A symbolic approach can extract a theory from a specific linguistic phenomenon, which raises at least three problems: first, determining whether a theory of a phenomenon should exist and in what terms; second, finding the appropriate logic for the extraction process; and third, ensuring the existence of an automatic method for extracting the theory in that logic. In this work, we have attempted to address the first and second points, and we did so using a logical formalism for which a solution to the third one already exists. Should this approach be successful, it can be used to address other text-related challenges, such as all variants of text classification. Additionally, our generalized 2-gram encoding can be further generalized to partially benefit from well-known word-to-vec approaches without compromising its symbolic essence. We will further verify our hypotheses by conducting some tests on an annotated newspaper corpus, which was human-labeled as metaphor or non-metaphor, consisting of 13,000 tokens and 2,000 different words. The label pertains to the entire text, and the task will regard recognizing its metaphoric expressions.