| Challenge: | In a typical lexicon, word senses are encoded as a list, without inter-sense relations. |
| Approach: | They propose a lexical resource which explicitly identifies the senses of a word's senses by expressing how they are derived from one another. |
| Outcome: | The proposed resource expresses how senses in the Open English Wordnet are derived from one another. |
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Latent semantic network induction in the context of linked example senses (D19-55)
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| Challenge: | Using the Princeton WordNet, we construct a network using the entirety of Wiktionary. |
| Approach: | They propose to use Wiktionary to construct a wordnet using the entirety of the open-source dictionary. |
| Outcome: | The proposed network induction process is similar to the Princeton WordNet, but with a more data-driven approach. |
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. |
Automatic Wordnet Mapping: from CoreNet to Princeton WordNet (L18-1)
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| Challenge: | Existing mappings focus on identifying the semantic categories of CoreNet, but not the word senses. |
| Approach: | They propose to map the word senses of CoreNet into Princeton WordNet synsets by lexical relations by a taxonomy. |
| Outcome: | The proposed mapping bridging the gap between CoreNet and WordNet shows that the word senses of CoreNet are mapped with precision of 91.2%. |
WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models (2024.lrec-main)
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| Challenge: | Lexical resources are a repository of knowledge and are used for many tasks, including word sense disambiguation and etymology. |
| Approach: | They compare WordNet, the most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
| Outcome: | The most commonly used lexical resource in NLP, with a variety of dictionaries and examples that were generated by ChatGPT. |
Frame Semantics across Languages: Towards a Multilingual FrameNet (C18-3)
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| Challenge: | This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics . |
| Approach: | This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics . |
| Outcome: | This workshop will present current research on aligning Frame Semantic resources across languages and automatic frame semantic parsing in English and other languages. |
Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy (2021.acl-long)
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| Challenge: | Existing models that represent different senses of words in context are not accurate for polysemous words. |
| Approach: | They propose a multilingual dataset that evaluates the ability of models to accurately represent different lexical-semantic relations such as homonymy and synonymy. |
| Outcome: | The proposed models can disambiguate homonyms in context, but fail to represent words with different senses when occurring in similar sentences. |
Browsing and Supporting Pluricentric Global Wordnet, or just your Wordnet of Interest (L18-1)
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| Challenge: | a wordnet browser that allows to consult wordnet content is presented in this paper . the paper presents a browser that meets design requirements and complies with the most ample range of design features. |
| Approach: | They propose a wordnet browser that meets design requirements for wordnets . they use existing browsers to analyze their functionalities and build a new browser . |
| Outcome: | The proposed browser meets design requirements and complies with the most ample range of design features. |
RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English) (2021.acl-long)
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| Challenge: | lexical ambiguity is a problem for NLP, but few tasks evaluate its impact on human intuitions. |
| Approach: | They propose to use contextualized word embeddings to evaluate word meaning . they use a dataset of human relatedness judgments and human estimates of sense dominance . |
| Outcome: | The proposed model matches human intuitions with contextualized embeddings on 112 ambiguous words in context with 672 sentence pairs. |
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. |
Annotating Chinese Word Senses with English WordNet: A Practice on OntoNotes Chinese Sense Inventories (2024.lrec-main)
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| Challenge: | a recent study has shown that large language models can be useful for cross-lingual applications. |
| Approach: | They propose to annotate Chinese word senses using English WordNet synsets . they examine the relationship between two annotators and find patterns among synset . |
| Outcome: | The proposed method shows that the annotators agree on 38% of the synsets compared with the original synset . the results highlight similarities between the synnotated synset and the WordNet structure . |