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It’s the Meaning That Counts: The State of the Art in NLP and Semantics

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Abstract

Semantics, the study of meaning, is central to research in Natural Language Processing (NLP) and many other fields connected to Artificial Intelligence. Nevertheless, how semantics is understood in NLP ranges from traditional, formal linguistic definitions based on logic and the principle of compositionality to more applied notions based on grounding meaning in real-world objects and real-time interaction. “Semantic” methods may additionally strive for meaningful representation of language that integrates broader aspects of human cognition and embodied experience, calling into question how adequate a representation of meaning based on linguistic signal alone is for current research agendas. We review the state of computational semantics in NLP and investigate how different lines of inquiry reflect distinct understandings of semantics and prioritize different layers of linguistic meaning. In conclusion, we identify several important goals of the field and describe how current research addresses them.

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Notes

  1. Simply stated, intensions refer to content and extensions to reference. So, while the intension of ‘the current chancelor of Germany’ is unchanging, its extension (currently, Angela Merkel) will change with time. Certain ‘extensional’ predicates can nevertheless be given intensional semantics: smart at a time t can be understood as \(\lambda \)x\(\in \)D.x is smart at t.

  2. Distributional models learn from the distribution of linguistic units in a text corpus, without necessitating external supervision.

  3. Basically defined, vagueness refers to a lexical item with more than one possible instantiation (e.g. “child”); polysemy to an item with different but related senses (e.g. “arms”); and hyponomy to an item that is a member of a broader class (e.g. “rose” to “flower”).

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    Daniel Hershcovich

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Hershcovich, D., Donatelli, L. It’s the Meaning That Counts: The State of the Art in NLP and Semantics. Künstl Intell 35, 255–270 (2021). https://doi.org/10.1007/s13218-021-00726-6

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