Challenge: Bantu languages are still computationally under-resourced due to their complex grammatical structure . morphological analyzers, text generation tools and a morphology analyzer are among the tools used .
Approach: They propose a syntactic and semantic method to disambiguate among singular nouns . they use the nearest neighbors of a query word as semantic generalizations based on Runyankore .
Outcome: The proposed method improves accuracy in three Bantu languages compared to using only the syntactic or semantic approach.

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Challenge: Pluralization of nouns is a challenge for the Bantu language family . results show that the language's definition of noune classes is inadequate for computational purposes due to non-determinism in prefixes.
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Challenge: a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics .
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The Interplay between Metaphors and NLP (2026.acl-tutorials)

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Challenge: This tutorial will provide an overview of the metaphor processing field.
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Challenge: Word Sense Disambiguation is a crucial task in Natural Language Processing . supervised systems need to be trained on word-by-word basis, a problem that is beyond reach for resource-rich languages like English.
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SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc (2025.naacl-long)

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Challenge: Recent studies show that language understanding offered by chat-based Large Language Models is limited and far from human-like performance.
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Towards a Standardized Dataset for Noun Compound Interpretation (L18-1)

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Challenge: Noun compounds are interesting constructs in Natural Language Processing . lack of standardized set of relation inventories and annotated datasets hinders interpretation .
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SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations (D19-1)

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Challenge: Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed.
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Challenge: WordNets are lexical databases in which groups of synonyms are stored according to the semantic relationships between them.
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