Drum Up SUPPORT : Systematic Analysis of Image-Schematic Conceptual Metaphors (2022)

Abstract: Conceptual metaphors represent a cognitive mechanism to transfer knowledge structures from one onto another domain. Image-schematic conceptual metaphors (ISCMs) specialize on transferring sensorimotor experiences to abstract domains. Natural language is believed to provide evidence of such metaphors. However, approaches to verify this hypothesis largely rely on top-down methods, gathering examples by way of introspection, or on manual corpus analyses. In order to contribute towards a method that is systematic and can be replicated, we propose to bring together existing processing steps in a pipeline to detect ISCMs, exemplified for the image schema SUPPORT in the COVID-19 domain. This pipeline consist of neural metaphor detection, dependency parsing to uncover construction patterns, clustering, and BERT-based frame annotation of dependent constructions to analyse ISCMs.

## introduction building on the foundation of existing knowledge to structure and explain new experiences is a common, well-known cognitive mechanism that, if depicted as metaphorical projection, can be captured by conceptual metaphors. in the case of image-schematic conceptual metaphors (iscms), the structures being transferred are sensorimotor patterns. natural language is considered a source of evidence for the existence of iscms, which has mostly been investigated by a top-down approach of introspectively identifying examples (e.g. @xcite ; @xcite ) or a bottom-up approach of corpus analyses (e.g. @xcite ). automated approaches generally focus on detecting whether a given sequence is metaphoric or not @xcite rather than identifying the specific type of metaphor, with few exceptions (e.g. @xcite ). however, effective computational tools for metaphor analysis are important as they can play a role in improving machine translation @xcite and in analyzing the usage and effect of metaphors, e.g. in political discourse @xcite or literature @xcite . in this paper, we propose a pipeline, depicted in fig. 1 , to automatically detect and identify iscms exemplified for the image schema support in an english covid-19 corpus. in contrast to introspective methods, the proposed pipeline promises to be replicable, faster, less subjective and capable of uncovering novel, previously unknown metaphors. extraction of supportrelated sentences, sec. 4.1 divide into metaphoric and literal sentences, sec. 4.2 dependency parsing and construct patterns, sec. 4.3 topic-based clustering and frame analysis, sec. 4.4 identify imageschematic conceptual metaphors for support, sec. 5.4 figure 1: overview of the proposed iscms analysis approach image schemas have been proposed by @xcite and @xcite as cognitive building blocks to capture recurring sensorimotor interactions with the physical world. these experiential patterns are said to "reveal features of human thought and language" @xcite , since they are mapped onto conceptual structures. iscms map these experiential, conceptual structures to the abstract domain. for instance, a person can physically lean on a concrete physical entity, e.g. a table, which entails the person pressing their body weight onto an entity that resists the push force. this physical experience can be mapped onto the abstract domain of emotional support, such as in he leans on his friends in these trying times. our approach relies on a series of steps to semi-automatically identify iscms in natural language: (a) detect whether a sequence is metaphoric or literal, (b) determine its constructional pattern, (c) identify its associated topics, and (d) identify its frames, from which we (e) derive underlying metaphoric projections. we extract sentences from the sample of the coronavirus corpus foot_0 based on seed words related to "support". in order to explore all metaphors related to support, we decided to chose a specific, abstract domain, i.e., covid-19 due to its ongoing relevance, abstract nature and importance to the society at large. with this first approach to "drum up" support for image schemas, this paper contributes a systematic method for detecting and identifying iscms in domain-specific natural language. to this end, constructional patterns uncover elements in a sentence that interact with metaphoric seed words, which are then frame annotated to provide evidence of the metaphor type the sentence represents. furthermore, we contribute to the analysis of conceptual metaphors in natural language in general since the pipeline can equally be applied to other types of metaphors, image schemas and domains. ## preliminaries within the tradition of embodied cognition, physical experiences are said to shape higher-level cognition, including natural language. for instance, we learn as infants that some objects can support our weight, such as a chair, while others cannot, such as a flower. this physical support can then be transferred in he leans on his friends in these trying times to emotional assistance depicted by the metaphor assistance is support. the proposed approach relies on theories of semantic frames and image schemas, which we briefly introduce. ## method as shown in fig. 1 , in order to identify imageschematic conceptual metaphors, we first compile a list of seed words related to "support", which we use to extract sentences from an english cor-pus on covid-19. each occurrence of a seed word in the corpus is automatically annotated as literal or metaphoric. with dependency parsing the constructional pattern for each sentence with metaphoric seed words are created. these patterns are important to identify the elements directly related to metaphoric seed words, for which we then obtain frame-semantic relations. the overall topic of each sentence is analyzed by way of clustering and frames and topics serve as a basis to identify its conceptual metaphor. ## discussion and conclusion in this paper, we presented a method to semiautomatically explore image-schematic conceptual metaphors, their related topics and constructional patterns in natural language. a pipeline returns syntactic patterns, thematic clusters, and frames for seed words related to a specific image schema. this approach enables the analysis of how the image schema support is used within the context of covid-19 in a more systematic and comprehensive way than possible with introspective methods. besides detecting examples of well-known metaphors, it allowed us to uncover new metaphors, e.g. recovery is bodily support. to this end, building constructional patterns in a bottom-up manner without prior assumptions was important. in terms of topics, a wide variety of supporters and support recipients could be detected. to apply the same method to other image schemas, a set of related seed words would need to be compiled as input to the method. for instance, a seed word list for the image schema con-tainment could include words such as "inside", "boundary", or "vessel". one drawback of this seed word approach is that polysemy in the sense of multiple literal or even metaphoric meanings of a seed words is not explicitly considered. nevertheless, given that no repositories of iscms exist and repositories on conceptual metaphors, such as metanet, contain a limited number of natural language examples or iscms, this semi-automated approach is an important step forward to drum up support of iscms. as a knowledge extraction approach rooted in cognitive science, a natural next step would be to explore the taxonomic structures of frames provided by metanet, framenet or similar resources to query interdependencies between and relations among iscms. furthermore, existing semantic resources, such as dbpedia and wikidata, should be utilized to increase the number of annotated frames. currently, this approach heavily relies on recent advances of methods in computational linguistics brought together in a pipeline. errors of one step are then propagated to the next. from the set of analysis steps, the metaphor detection performed best. the part-of-speech tags assigned in the process of dependency parsing are highly problematic for specific seed words, such as "back" that is frequently mistagged as noun or adverb when used as verb, negatively affecting the obtained dependency relations and constructional patterns. another shortcoming is that the clustering method groups many samples into a cluster of outliers. the number of identified outliers, however, is so large that valuable information is inevitably lost, and only a subpart of the semantic topics is represented in the results. for the frame parsing, the return of frames per sentence was considerably higher than with similar approaches (as also reported in (minnema and nissim, 2021)), however, the number of frames for seed words was less than half of the overall count of sentences. very short, headinglike sequences that lack context were generally not frame-annotated at all, e.g. standing in line for essentials. this reinforces the need to supplement frame parsing with other processes and resources. to improve the pipeline and reduce the amount of manual labor required, it would be beneficial to be able to automatically label the target domains for which a specific image schema is used -a step that currently is mostly done manually with the resulting frames and clusters, due to the lack of frame coverage. in order to automatically identify the target domain, we plan to train a sequence-tosequence model, e.g. t5 @xcite , to predict the target domain given a source domain and a contextualizing sentence. for instance, the sample support: he leans on his friends in these trying times should be labeled as assistance.