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Structured Metaphor and Metonymy in WordNet: ChainNet, a Lexical Resource for Modeling Polysemy

Core Concepts
ChainNet is a new lexical resource that explicitly identifies the structured relationships between word senses, including metaphorical and metonymic connections, in the Open English WordNet.
The content presents ChainNet, a new lexical resource that formalizes the structured relationships between word senses in WordNet. Key highlights: Words can exhibit polysemy, where a single word has multiple distinct meanings. These meanings can be related through metaphor or metonymy, or they can be completely unrelated (homonymy). ChainNet represents these relationships, with every nominal sense of a word being either a prototypical sense or derived from another sense via metaphor or metonymy. For metaphorical extensions, ChainNet also records the feature transformations that occur, capturing how the meaning changes. The authors collected annotations for 6,500 words in WordNet using three annotators, and report on the inter- and intra-annotator agreement for the task. The authors also present two baseline models for the task of "polysemy parsing", which aims to automatically synthesize ChainNet annotations for the rest of WordNet. The authors reflect on insights gained from applying the theory of chaining to real-world data, including the prevalence of "multistable" word meanings and the distinction between cognitive and etymological chains.
"March always has dreadful rainfall!" "The infantry performed a militant march." "The march approached the town hall." "Nothing can stop the march of science."
"Chaining can cause word meaning to extend into disjointed areas of semantic space (Austin, 1961, p. 72): the academic progress in (4) and the regimented walking in (2) have little in common, even though they are connected in a chain." "Unlike other metaphor resources, ChainNet provides a representation of the meaning change that is caused by each metaphor. This takes the form of a feature transformation."

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by Rowan Hall M... at 04-01-2024

Deeper Inquiries

How could the ChainNet annotations be leveraged to improve natural language understanding models

The ChainNet annotations can significantly enhance natural language understanding models by providing a deeper understanding of word senses and their relationships. By incorporating the information from ChainNet, models can better capture the nuances of polysemy, metaphor, and metonymy in language. This enriched data can help in improving tasks such as word sense disambiguation, sentiment analysis, machine translation, and information retrieval. Models can leverage the feature transformations provided in ChainNet to better understand how metaphors modify the meaning of senses, leading to more accurate and contextually relevant interpretations of text. Additionally, the structured nature of ChainNet annotations can aid in developing more sophisticated language models that can handle complex linguistic phenomena.

What are the limitations of the metaphor and metonymy taxonomy used in ChainNet, and how could it be expanded or refined

The metaphor and metonymy taxonomy used in ChainNet, while valuable, has certain limitations that could be addressed for further refinement. One limitation is the binary classification of senses into metaphorical or metonymical categories, which may oversimplify the complex nature of semantic relationships. To enhance the taxonomy, a more nuanced classification system could be developed to capture different degrees of metaphorical or metonymical extension. Additionally, incorporating additional categories such as synecdoche, folk-etymology, and other types of semantic shifts could provide a more comprehensive understanding of sense relations. Furthermore, refining the taxonomy to account for conceptual metaphors and logical metonymy patterns could offer a more holistic view of how meaning evolves in language.

How do the cognitive and etymological chains of word meaning relate to the psychological processes of language acquisition and change over time

The cognitive and etymological chains of word meaning offer insights into the psychological processes of language acquisition and change over time. Cognitive chains reflect how speakers perceive and interpret word meanings based on their mental representations and experiences, leading to the formation of metaphorical and metonymical connections. These cognitive chains are dynamic and can vary among individuals, contributing to the richness and diversity of language usage. On the other hand, etymological chains trace the historical development of word senses, highlighting the evolution of language over time. The relationship between cognitive and etymological chains showcases the interplay between individual cognition and linguistic evolution, shedding light on how language adapts and transforms through cultural, social, and cognitive influences. Understanding these chains can provide valuable insights into the intricate mechanisms of language processing and change.