UWB at SemEval-2026 Task 5: Synsets and their contexts

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

The UWB system participated in SemEval-2026 Task 5, a challenge that required developing a program to match human ratings of sense-appropriateness for a particular word embedded within a series of very structured, short stories. The system's methodology involved associating a fixed list of 50 words with each WordNet synset. It then computed several scores for each phrase in the story to determine how closely that phrase matched the established wordlist. Despite exploring several different approaches to building and employing these sets of word-lists, the UWB system ultimately received near-chance results. The authors concluded that their system's approach was inappropriate for this specific dataset, which is intentionally designed to be ambiguous, with every story containing words associated with at least two senses of the target word.

Key takeaway

For NLP Engineers developing word sense disambiguation systems, this result suggests that relying solely on fixed wordlists associated with WordNet synsets may be insufficient for highly ambiguous datasets. If your task involves nuanced contextual understanding, you should prioritize approaches that dynamically model context rather than static lexical associations. Consider evaluating your system's robustness against datasets specifically designed for ambiguity early in development.

Key insights

The UWB system's wordlist-based approach failed SemEval-2026 Task 5 due to dataset ambiguity.

Principles

• Ambiguous datasets challenge fixed wordlist approaches.
• Word sense disambiguation requires context-aware methods.

Method

Associates a fixed list of 50 words with each WordNet synset, then computes scores for story phrases against these wordlists.

In practice

• Avoid fixed wordlists for highly ambiguous contexts.
• Evaluate system suitability against dataset design.

Topics

• SemEval-2026 Task 5
• Word Sense Disambiguation
• WordNet Synsets
• Lexical Ambiguity
• NLP Evaluation
• Contextual Modeling

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.