| Challenge: | Existing word usage graphs lack human interpretability of senses. |
| Approach: | They propose to enrich existing word usage graphs with cluster labels functioning as sense definitions. |
| Outcome: | The proposed dataset matches the definitions chosen from WordNet by two baseline systems. |
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More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages (2024.emnlp-main)
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Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte Im Walde, Nina Tahmasebi
| Challenge: | Word Usage Graphs (WUGs) represent word sense clusters from simple pairwise word use judgments. |
| Approach: | They propose to use a weighted graph to represent human semantic proximity judgments for pairs of word uses to infer word sense clusters from simple pairwise word use judgments. |
| Outcome: | The proposed approach can be applied in a Word Sense Induction (WSI) setting or for Word sense disambiguation (WSD) it is the first and to date largest manually annotated, diachronic WUG dataset. |
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis (2023.acl-long)
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| Challenge: | Existing approaches to semantic change analysis are limited in their interpretation power and lack of explanatory power. |
| Approach: | They propose to use specialised Flan-T5 language models to generate a definition for each usage and a specialised word sense model to generate the most prototypical definition. |
| Outcome: | The proposed representations outperform token or usage sentence embeddings in word-in-context semantic similarity judgements and are a promising type of lexical representation for NLP. |
Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)
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| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
| Approach: | They propose to use word embeddings, text generators, context encoders to extract underlying knowledge graphs of nine influential language models. |
| Outcome: | The proposed model is able to encode word embeddings, text generators, and context encoders, but suffers from several inaccuracies. |
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
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| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
| Outcome: | the 2013 edition of TextGraphs is being held in conjunction with the 9th International Joint Conference on Natural Language Processing in Hong Kong. |
DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages (2021.emnlp-main)
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| Challenge: | Existing methods for graded contextual word meaning annotation have not been implemented yet. |
| Approach: | They propose a multi-round incremental annotation process and a clustering algorithm to group usages into senses to create a large-scale dataset. |
| Outcome: | The proposed method is the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. |
A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches (L18-1)
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| Challenge: | WordNets are lexical databases in which groups of synonyms are stored according to the semantic relationships between them. |
| Approach: | This paper describes various approaches to constructing WordNets automatically by leveraging traditional lexical resources and newer trends such as word embeddings. |
| Outcome: | The proposed methods leverage traditional lexical resources and newer trends such as word embeddings to build and evaluate WordNets. |
Explaining novel senses using definition generation with open language models (2025.findings-emnlp)
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| Challenge: | We apply definition generators based on open-weights large language models to create explanations of novel senses. |
| Approach: | They apply open-weights large language models to create explanations of novel senses using target word usages as input. |
| Outcome: | The proposed definition generators perform on par with decoder-only models. |
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages (2020.acl-main)
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| Challenge: | Existing methods to induce word senses from raw sentences lack reliable and high-coverage distributions. |
| Approach: | They propose an automatic and multilingual approach to inducing word senses from a corpus of raw sentences using an annotated corpus. |
| Outcome: | The proposed method outperforms all other methods on English and other languages. |
On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping (N19-1)
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| Challenge: | Sense representations target meaning conflation deficiency but their potential impact has not been investigated in downstream NLP applications. |
| Approach: | They propose to use a reverse dictionary system to address meaning conflation deficiency . they propose to integrate senses into the system to improve semantic understanding . |
| Outcome: | The proposed approach can improve the performance of a downstream NLP application. |
Multi-Sense Embeddings for Language Models and Knowledge Distillation (2025.findings-acl)
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| Challenge: | Transformer-based large language models generate different representations for the same token depending on context . however, words and tokens typically have only a limited number of senses . a knowledge distillation method can be used to learn a smaller student model . |
| Approach: | They propose a multi-sense embedding method that uses a clustering algorithm to generate a sense embeddable dictionary. |
| Outcome: | The proposed method offers significant space and inference time savings while maintaining competitive performance. |