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Related Experiment Video

Updated: Dec 7, 2025

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Sense identification data: A dataset for lexical semantics.

Davide Colla1, Enrico Mensa1, Daniele P Radicioni1

  • 1Computer Science Department, University of Turin, Italy.

Data in Brief
|September 28, 2020
PubMed
Summary
This summary is machine-generated.

The Sense Identification Dataset (SID) enables systems to identify the specific word senses humans use when rating conceptual similarity. This new dataset is crucial for advancing semantic similarity research and understanding lexical access.

Keywords:
Lexical processingLexical semanticsSemantic similaritySense annotationSense embeddingsSense individuationSimilarity metricsWord embeddings

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Assessing conceptual similarity between terms requires understanding the specific senses humans employ.
  • Existing semantic similarity tasks lack explicit annotation of the senses used in human ratings.
  • Investigating human lexical access strategies necessitates identifying the senses involved in similarity judgments.

Purpose of the Study:

  • To introduce the Sense Identification Dataset (SID), the first dataset specifically designed for the sense identification task.
  • To provide a standardized experimental ground for systems and approaches addressing sense identification.
  • To enable a more thorough assessment of semantic similarity ratings by clarifying the senses involved.

Main Methods:

  • Manually annotated term pairs from the SemEval-2017 Task 2 English dataset with BabelNet sense identifiers.
  • Ensured annotated senses are compatible with the existing human similarity ratings from the original dataset.
  • Utilized BabelNet sense identifiers for broad compatibility with resources like WordNet and Wikidata.

Main Results:

  • The creation of the Sense Identification Dataset (SID), featuring manually annotated sense pairs.
  • The SID dataset provides BabelNet identifiers, facilitating integration with other lexical resources.
  • The dataset enables the evaluation of systems performing the sense identification task.

Conclusions:

  • The SID dataset is a vital resource for advancing research in sense identification and semantic similarity.
  • It allows for a deeper understanding of how humans access and compare word senses.
  • The dataset supports further Natural Language Processing tasks by providing explicit sense annotations.