| Challenge: | Word senses are typically defined with textual definitions and put in context via lexical-semantic relations such as synonymy, antonymy, hypernymy, etc. |
| Approach: | They propose a slot-filler structure to define the meaning of words in terms of their prototypical semantic information. |
| Outcome: | The proposed model improves on a semantic similarity task and shows significant improvements over state-of-the-art embeddings. |
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Automatically Generated Definitions and their utility for Modeling Word Meaning (2024.emnlp-main)
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| Challenge: | Modern language models generate semantic representations for words based on context and context based models. |
| Approach: | They propose to use dictionary-like sense definitions to generate sentence embeddings . they evaluate the quality of the generated definitions on existing English benchmarks based on the results of their study . |
| Outcome: | The proposed model sets new state-of-the-art results on lexical semantics tasks compared to baselines . |
Enriching Word Usage Graphs with Cluster Definitions (2024.lrec-main)
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| 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. |
Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling (D19-1)
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| Challenge: | Existing definition modeling methods do not utilize lexical semantic relations between defined words and defining words. |
| Approach: | They propose definition modeling methods that use lexical semantic relations . they use unsupervised pattern-based word-pair embeddings that represent semantic relations of word pairs . |
| Outcome: | The proposed methods improve definition generation and learning embeddings from definitions. |
Feature2Vec: Distributional semantic modelling of human property knowledge (D19-1)
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| Challenge: | Existing distributional semantic models of word meaning are limited in size due to constraints associated with exhaustively listing properties for large numbers of words. |
| Approach: | They propose a method for mapping human property knowledge onto a distributional semantic space and adapt it to the task of modelling concept features. |
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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. |
AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings (2024.lrec-main)
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| Challenge: | Contextualised Language Models (LMs) improve on word embeddings by encoding meaning of words in context. |
| Approach: | They propose to learn a unified embedding space in which all three types of representations can be integrated. |
| Outcome: | The proposed model outperforms existing approaches in ontology completion tasks. |
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment (P18-1)
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| Challenge: | SemAxis characterizes word semantics using many semantic axes in word-vector spaces beyond sentiment . lexicon-based text analysis assumes that meaning of words does not change across contexts . but, recent advances in vector-space representations can tackle this challenge . |
| Approach: | They propose a framework to characterize word semantics using many semantic axes beyond sentiment . they demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities . |
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Querying Word Embeddings for Similarity and Relatedness (N18-1)
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| Challenge: | Word2Vec embeddings have become popular representations of word meaning . similarity between two words is often assumed to be a direction-less measure, whereas relatedness is inherently directional. |
| Approach: | They propose to use word embeddings to predict asymmetric association between words from a dataset of production norms to generate thematically related words. |
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Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks (2023.eacl-main)
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| Challenge: | Recent work on word embeddings reports low correlations with human ratings . contextual language models (CLMs) have been successful in acquiring semantic and world knowledge. |
| Approach: | They propose to use BERT to probe contextual language models for predicting typicality scores. |
| Outcome: | The proposed methods improve on previous studies on word embeddings and their ability to predict typicality scores. |
WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (N19-1)
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| Challenge: | Existing word embeddings cannot model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings. |
| Approach: | They propose a large-scale Word in Context dataset, called WiC, which is curated by experts and can be used to evaluate context-sensitive representations. |
| Outcome: | The proposed models outperform the standard evaluation dataset for the purpose and highlight their shortcomings. |