| Challenge: | LanguageNet is a system that can help second language learners to search for different meanings and usages of a word . the polysemy of words, namely words with more than one sense, is one of the major challenges for ESOL learners . |
| Approach: | They propose a system which can help second language learners to search for different meanings of a word. |
| Outcome: | The proposed system can help second language learners to search for different meanings and usages of a word. |
Similar Papers
Annotating Chinese Word Senses with English WordNet: A Practice on OntoNotes Chinese Sense Inventories (2024.lrec-main)
Copied to clipboard
| 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 . |
Latent semantic network induction in the context of linked example senses (D19-55)
Copied to clipboard
| 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. |
Multilingual Substitution-based Word Sense Induction (2024.lrec-main)
Copied to clipboard
| Challenge: | Word Sense Induction is the task of finding senses of an ambiguous word . many approaches to WSI are language-specific and are not easily adaptable to new languages. |
| Approach: | They propose to use multilingual substitution-based WSI methods that generalize to any language supported by the underlying multilingual language model with minimal to no adaptation required. |
| Outcome: | The proposed methods perform on par with monolingual approaches on popular English datasets while being language-specific. |
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
Copied to clipboard
| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
| Approach: | This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics . |
| Outcome: | This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics . |
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment (2020.lrec-1)
Copied to clipboard
Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Andrea Bellandi, Irene Pisani, Thomas Troelsgård, Sussi Olsen, Simon Krek, Veronika Lipp, Tamás Váradi, László Simon, András Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, José Luis Sancho, Rafael-J. Ureña-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stanković, Andrej Perdih, Dejan Gabrovsek
| Challenge: | a new dataset aims to align monolingual dictionaries with a single sense level for 15 languages . this dataset covers a wide range of languages and resources . |
| Approach: | They propose to manually align monolingual dictionaries with possible semantic relationships . they use 15 languages to create a new baseline for the task of monolingual word sense alignment . |
| Outcome: | The proposed dataset covers 15 languages and covers the more challenging task of linking general-purpose language. |
Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation (L18-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation is a crucial task in Natural Language Processing . supervised systems need to be trained on word-by-word basis, a problem that is beyond reach for resource-rich languages like English. |
| Approach: | They release six large-scale sense-annotated datasets in multiple languages to pave the way for supervised multilingual Word Sense Disambiguation. |
| Outcome: | The results show that large-scale sense annotations can be used as training sets for supervised systems. |
Improving HowNet-Based Chinese Word Sense Disambiguation with Translations (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work on unsupervised WSD has leveraged lexical knowledge bases, such as WordNet and BabelNet, but these have proven to be less effective for Chinese. |
| Approach: | They propose a system which combines contextual information from a pretrained neural language model with bilingual information obtained via machine translation and sense translation information from HowNet. |
| Outcome: | The proposed system achieves a state-of-the-art for unsupervised Chinese WSD. |
A Dataset of Translational Equivalents Built on the Basis of plWordNet-Princeton WordNet Synset Mapping (2020.lrec-1)
Copied to clipboard
| Challenge: | a dataset of 11,000 Polish-English translational equivalents is presented . the dataset is a novum in the wordnet domain and can facilitate the precision of bilingual NLP tasks. |
| Approach: | They present a dataset of Polish-English translational equivalents linked by three types of equivalence links. |
| Outcome: | The proposed dataset contains 11,000 Polish-English translational equivalents . the resulting subsets are based on a manual annotation process and a set of formal features . |
CLUSE: Cross-Lingual Unsupervised Sense Embeddings (D18-1)
Copied to clipboard
| Challenge: | Existing models for learning bilingual sense embeddings that encode semantics are weak for recognizing multi-sense word representations . elucidation of word embeddables is difficult because they do not allow a word to have different meanings in different contexts. |
| Approach: | They propose a sense induction and representation learning model that learns bilingual sense embeddings that align well in the vector space. |
| Outcome: | The proposed model shows that the learned embeddings are aligned well in the vector space. |
Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy (2021.acl-long)
Copied to clipboard
| 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. |