CALE : Concept-Aligned Embeddings for Both Within-Lemma and Inter-Lemma Sense Differentiation

• URL: http://arxiv.org/abs/2508.04494v1
• Date: Wed, 06 Aug 2025 14:43:22 GMT
• Title: CALE : Concept-Aligned Embeddings for Both Within-Lemma and Inter-Lemma Sense Differentiation
• Authors: Bastien Liétard, Gabriel Loiseau,
• Abstract summary: Lexical semantics is concerned with both the multiple senses a word can adopt in different contexts, and the semantic relations that exist between meanings of different words.<n>To investigate them, Contextualized Language Models are a valuable tool that provides context-sensitive representations.<n>We propose an extension, Concept Differentiation, to include inter-words scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lexical semantics is concerned with both the multiple senses a word can adopt in different contexts, and the semantic relations that exist between meanings of different words. To investigate them, Contextualized Language Models are a valuable tool that provides context-sensitive representations that can be used to investigate lexical meaning. Recent works like XL-LEXEME have leveraged the task of Word-in-Context to fine-tune them to get more semantically accurate representations, but Word-in-Context only compares occurrences of the same lemma, limiting the range of captured information. In this paper, we propose an extension, Concept Differentiation, to include inter-words scenarios. We provide a dataset for this task, derived from SemCor data. Then we fine-tune several representation models on this dataset. We call these models Concept-Aligned Embeddings (CALE). By challenging our models and other models on various lexical semantic tasks, we demonstrate that the proposed models provide efficient multi-purpose representations of lexical meaning that reach best performances in our experiments. We also show that CALE's fine-tuning brings valuable changes to the spatial organization of embeddings.

Related papers

• Tomato, Tomahto, Tomate: Measuring the Role of Shared Semantics among Subwords in Multilingual Language Models [88.07940818022468]
  We take an initial step on measuring the role of shared semantics among subwords in the encoder-only multilingual language models (mLMs) We form "semantic tokens" by merging the semantically similar subwords and their embeddings. inspections on the grouped subwords show that they exhibit a wide range of semantic similarities.
  arXiv  Detail & Related papers  (2024-11-07T08:38:32Z)
• Evaluating Distributed Representations for Multi-Level Lexical Semantics: A Research Proposal [3.3585951129432323]
  This thesis builds a bridge between computational models and lexical semantics, aiming to complement each other.<n>Modern neural networks (NNs) construct distributed representations by compressing individual words into dense, continuous, high-dimensional vectors.
  arXiv  Detail & Related papers  (2024-06-02T14:08:51Z)
• Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity [68.8204255655161]
  We present the semantic notion of agentivity as a case study for probing such interactions. This suggests LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery.
  arXiv  Detail & Related papers  (2023-05-29T16:24:01Z)
• Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution [124.99894592871385]
  We present a large-scale comparative study of lexical substitution methods employing both old and most recent language models. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly.
  arXiv  Detail & Related papers  (2022-06-07T16:16:19Z)
• IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations [0.0]
  We present our findings based on the descriptive, exploratory, and predictive data analysis conducted on the CODWOE dataset. We give a detailed overview of the systems that we designed for Definition Modeling and Reverse Dictionary tasks.
  arXiv  Detail & Related papers  (2022-05-13T18:15:20Z)
• Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation [56.830395467247016]
  We propose a model of semantic memory for WSD in a meta-learning setting. Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork. We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce scenarios.
  arXiv  Detail & Related papers  (2021-06-05T20:40:01Z)
• EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses [0.0]
  We leverage the rich semantic structures in WordNet to enhance the quality of multi-sense embeddings. We derive new distributional semantic similarity measures for M-SE from prior ones. We report evaluation results on 11 benchmark datasets involving WSD and Word Similarity tasks.
  arXiv  Detail & Related papers  (2021-02-27T14:36:55Z)
• Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks [58.87961226278285]
  We propose a self-supervised approach to model lexical semantic change. We show that our method can be used for the detection of semantic change with any alignment method. We illustrate the utility of our techniques using experimental results on three different datasets.
  arXiv  Detail & Related papers  (2021-01-30T18:59:43Z)
• Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards [13.753240692520098]
  We present a neural network architecture for joint coreference resolution and semantic role labeling for English. We use reinforcement learning to encourage global coherence over the document and between semantic annotations. This leads to improvements on both tasks in multiple datasets from different domains.
  arXiv  Detail & Related papers  (2020-10-12T09:36:24Z)
• Analysing Lexical Semantic Change with Contextualised Word Representations [7.071298726856781]
  We propose a novel method that exploits the BERT neural language model to obtain representations of word usages. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements.
  arXiv  Detail & Related papers  (2020-04-29T12:18:14Z)
• Multiplex Word Embeddings for Selectional Preference Acquisition [70.33531759861111]
  We propose a multiplex word embedding model, which can be easily extended according to various relations among words. Our model can effectively distinguish words with respect to different relations without introducing unnecessary sparseness.
  arXiv  Detail & Related papers  (2020-01-09T04:47:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.