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    Home/Original/inverse
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    Inverse View

    It is not the case that Broad-coverage statistical word sense disambiguation tools remain elusive

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    Reasons For

    2 perspectives
    Reason for 1 of 2
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    • 1.Large-scale language models like BERT achieve near-human WSD performance on benchmark tasks by leveraging contextual embeddings across broad vocabularies.
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    • 2.When a system matches human inter-annotator agreement on WSD benchmarks, the claim of elusiveness collapses into a question about evaluation criteria, not capability.
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    Reason for 2 of 2
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    • 1.Wittgenstein's argument that word meaning is use implies discrete sense inventories like WordNet artificially multiply the 'disambiguation problem' beyond what cognition requires.
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    • 2.If the problem is partly an artifact of lexicographic sense enumeration rather than natural language structure, the elusiveness of broad-coverage WSD reflects a methodological choice, not an empirical barrier.
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    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.Thousands of words have multiple senses in sources such as WordNet
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    • 2.Constructing a sense-annotated training corpus with sufficiently many occurrences of all senses is difficult
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    • 3.Annotations are therefore typically restricted to the senses of a few polysemous words
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