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Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)

Muhammad Asif Ali, Yan Hu, Jianbin Qin, Di Wang

TL;DR

This work tackles the problem of distinguishing antonyms from synonyms by explicitly modeling relation-specific properties using ICE-NET. The framework interlaces three encoders: ENC-1 captures synonym symmetry, ENC-2 captures antonym symmetry, and ENC-3 uses attentive graph convolutions on word graphs to preserve transitivity and trans-transitivity. The model leverages pre-trained embeddings and a graph-based propagation scheme, trained end-to-end with a combined loss L1+L2+L3. Empirical results on two benchmark datasets show ICE-NET achieves up to $1.8\%$ relative improvements in F1 over prior methods and demonstrates robustness across word classes and language variants. The work introduces the first use of attentive graph convolutions for antonym-synonym distinction and highlights practical insights for graph construction, attention weighting, and error analysis, with code made publicly available.

Abstract

Antonyms vs synonyms distinction is a core challenge in lexico-semantic analysis and automated lexical resource construction. These pairs share a similar distributional context which makes it harder to distinguish them. Leading research in this regard attempts to capture the properties of the relation pairs, i.e., symmetry, transitivity, and trans-transitivity. However, the inability of existing research to appropriately model the relation-specific properties limits their end performance. In this paper, we propose InterlaCed Encoder NETworks (i.e., ICE-NET) for antonym vs synonym distinction, that aim to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classification task in a performance-enhanced manner. Experimental evaluation using the benchmark datasets shows that ICE-NET outperforms the existing research by a relative score of upto 1.8% in F1-measure. We release the codes for ICE-NET at https://github.com/asif6827/ICENET.

Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET)

TL;DR

This work tackles the problem of distinguishing antonyms from synonyms by explicitly modeling relation-specific properties using ICE-NET. The framework interlaces three encoders: ENC-1 captures synonym symmetry, ENC-2 captures antonym symmetry, and ENC-3 uses attentive graph convolutions on word graphs to preserve transitivity and trans-transitivity. The model leverages pre-trained embeddings and a graph-based propagation scheme, trained end-to-end with a combined loss L1+L2+L3. Empirical results on two benchmark datasets show ICE-NET achieves up to relative improvements in F1 over prior methods and demonstrates robustness across word classes and language variants. The work introduces the first use of attentive graph convolutions for antonym-synonym distinction and highlights practical insights for graph construction, attention weighting, and error analysis, with code made publicly available.

Abstract

Antonyms vs synonyms distinction is a core challenge in lexico-semantic analysis and automated lexical resource construction. These pairs share a similar distributional context which makes it harder to distinguish them. Leading research in this regard attempts to capture the properties of the relation pairs, i.e., symmetry, transitivity, and trans-transitivity. However, the inability of existing research to appropriately model the relation-specific properties limits their end performance. In this paper, we propose InterlaCed Encoder NETworks (i.e., ICE-NET) for antonym vs synonym distinction, that aim to capture and model the relation-specific properties of the antonyms and synonyms pairs in order to perform the classification task in a performance-enhanced manner. Experimental evaluation using the benchmark datasets shows that ICE-NET outperforms the existing research by a relative score of upto 1.8% in F1-measure. We release the codes for ICE-NET at https://github.com/asif6827/ICENET.
Paper Structure (33 sections, 6 equations, 3 figures, 6 tables, 1 algorithm)

This paper contains 33 sections, 6 equations, 3 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Preliminaries
  5. KG Embeddings Methods
  6. Proposed Approach
  7. ENC-1
  8. ENC-2
  9. ENC-3
  10. Graph Construction.
  11. Attentive Aggregation.
  12. The Complete Model.
  13. Experiments and Results
  14. Datasets
  15. Experimental Settings
  16. ...and 18 more sections

Figures (3)

  • Figure 1: (a) Properties of the antonym and synonym relation pairs, i.e., symmetry, transitivity, and trans-transitivity; (b) Limitation of translational embeddings in capturing the antonym and synonym relations ali2019.
  • Figure 2: Illustration of attentive Graph Convolution Networks
  • Figure : Graph Construction