Papers with Word Sense Disambiguation
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
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| 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 . |
Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation (2022.naacl-srw)
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| Challenge: | Word Sense Disambiguation (WSD) is a core task in natural language processing . ancient Chinese has rarely been used in WSD tasks due to lack of a dataset . |
| Approach: | They annotate ancient Chinese text Zuo Zhuan using a copyright-free dictionary . they apply a method to find the most appropriate sense in a context using k-NN . |
| Outcome: | The proposed dataset will be available on GitHub. |
A Deep Dive into Word Sense Disambiguation with LSTM (C18-1)
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| Challenge: | LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD) but neither the training data nor the source code was released. |
| Approach: | They propose to use LSTM-based language models to perform Word Sense Disambiguation (WSD) using openly available datasets and software. |
| Outcome: | The proposed method returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code were released. |
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)
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| Challenge: | Word Sense Disambiguation (WSD) is a task of associating a word in context with its most appropriate sense from a predefined sense inventory. |
| Approach: | They propose to use a state-of-the-art neural model to integrate WSD into real-world applications. |
| Outcome: | The proposed system offers high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. |
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary (2021.eacl-main)
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| Challenge: | Existing models for Word Sense Disambiguation struggle to disambiguate rare senses . current models struggle to learn senses with few training examples . |
| Approach: | They introduce a low-shot WSD dataset automatically extracted from example sentences in Wiktionary. |
| Outcome: | The proposed dataset outperforms baseline models on rare senses in existing datasets. |
Towards General-Domain Word Sense Disambiguation: Distilling Large Language Model into Compact Disambiguator (2025.emnlp-main)
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| Challenge: | Existing methods for Word Sense Disambiguation rely heavily on manually annotated data, which limits coverage and generalization. |
| Approach: | They propose a framework that leverages large language models as knowledge distillers to build silver-standard WSD corpora by combining generation-based distillation and annotation-based disambiguation. |
| Outcome: | The proposed framework outperforms existing methods on general-domain benchmarks by 50% on the most challenging test set and by 1000 times fewer parameters. |
Just “OneSeC” for Producing Multilingual Sense-Annotated Data (P19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is one of the most affected research areas . annotated data are scarce in English and almost absent in other languages . |
| Approach: | They propose a language-independent method for the automatic extraction of thousands of sentences in which a target word is tagged with its meaning. |
| Outcome: | The proposed method outperforms existing methods on multilingual and domain-specific settings. |
IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages (2021.emnlp-main)
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Rexhina Blloshmi, Tommaso Pasini, Niccolò Campolungo, Somnath Banerjee, Roberto Navigli, Gabriella Pasi
| Challenge: | Recent advances in contextualized embeddings have made ranking on non-English documents cumbersome . a novel multilingual query expansion mechanism provides sense definitions as additional semantic information for the query. |
| Approach: | They propose a multilingual query expansion mechanism that leverages word sense information to enhance the model's performance. |
| Outcome: | The proposed model performs better than its supervised and unsupervised alternatives across languages while being trained on English Robust04 data. |
Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models (2024.lrec-main)
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Lorenzo Proietti, Stefano Perrella, Simone Tedeschi, Giulia Vulpis, Leonardo Lavalle, Andrea Sanchietti, Andrea Ferrari, Roberto Navigli
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) but current pretrained language models lack the granularity to perform disambiguation . |
| Approach: | They propose a large-scale resource that leverages homonymy relations to cluster WordNet senses and train Homonymy Disambiguation systems. |
| Outcome: | The proposed model can distinguish homonyms with up to 95% accuracy even without fine-tuning the underlying PLM. |
Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders (2020.acl-main)
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| Challenge: | Existing models for Word Sense Disambiguation are not uniformly distributed on rare or unseen senses. |
| Approach: | They propose a bi-encoder model that embeds the target word with its context and the dictionary definition, or gloss, of each sense. |
| Outcome: | The proposed model outperforms previous state-of-the-art models on English all-words WSD, with a 31.1% error reduction on less frequent senses over prior work. |
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation (2022.findings-emnlp)
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| Challenge: | Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. |
| Approach: | They propose a word-level mixup method that maintains the sense of a target word by increasing the frequency of least frequent senses during training to reduce the distributional bias of senses. |
| Outcome: | The proposed method shows high accuracy on the most frequent sense (MFS) and low accuracy on least frequent senses (LFS). |
ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension (2021.emnlp-main)
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| Challenge: | Existing systems for word Sense Disambiguation assume that each word can be disambiguated individually . a novel approach to WSD is proposed to address this limitation . |
| Approach: | They propose a supervised semantics-based approach to Word Sense Disambiguation that takes into account the senses assigned to nearby words. |
| Outcome: | The proposed approach surpasses all its competitors and sets a new state of the art on English WSD. |
How Much Do Encoder Models Know About Word Senses? (2025.acl-long)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) however, how well these models inherently disambiguate word senses remains uncertain. |
| Approach: | They evaluate several encoder-only PLMs across WordNet and ODE sense inventories to evaluate their ability to separate word senses without any task-specific fine-tuning. |
| Outcome: | The proposed model outperforms output layer on WordNet and ODE sense inventories by 15 percentage points. |
LTRS: Improving Word Sense Disambiguation via Learning to Rank Senses (2025.coling-main)
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| Challenge: | Conventional training strategies only consider predefined senses for target words and learn each of them from relatively limited instances, neglecting the influence of similar ones. |
| Approach: | They propose a method to rank senses to improve the task of word Sense Disambiguation (WSD) by ranking an expanded list of sense definitions. |
| Outcome: | The proposed method achieves a SOTA F1 score of 79.6% in Chinese WSD and shows faster convergence than previous methods. |
WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context (2021.eacl-main)
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| Challenge: | Existing benchmarks for Word Sense Disambiguation are limited to those systems in which sense distinctions are defined according to an underlying sense inventory. |
| Approach: | They propose a framework for Target Sense Verification of Words in Context which grounds its uniqueness as binary classification task and independent of external sense inventories. |
| Outcome: | The proposed framework is highly flexible for evaluation of diverse models and systems in and across domains. |
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources (L18-1)
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| Challenge: | Existing supervised models for Word Sense Disambiguation (WSD) are limited to knowledge-based approaches. |
| Approach: | They propose to use WordNet and WordNet Domains to enhance supervised WSD models by introducing semantic features into the classifiers and using the SLR structure to augment training data. |
| Outcome: | The proposed model improves the state-of-the-art in Word Sense Disambiguation (WSD) The proposed approach is compared with the state of the art in the most popular benchmarks. |
Together We Make Sense–Learning Meta-Sense Embeddings (2023.findings-acl)
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| Challenge: | Existing sense embeddings do not cover all senses of ambiguous words equally well due to discrepancies in their training resources. |
| Approach: | They propose a meta-sense embedding method that preserves sense neighbourhoods by combining multiple independently trained source sense embeddables. |
| Outcome: | The proposed method outperforms several baselines on Word Sense Disambiguation and Word-in-Context tasks. |
UFSAC: Unification of Sense Annotated Corpora and Tools (L18-1)
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| Challenge: | a dozen sense annotated English corpora are used in Word Sense Disambiguation (WSD) a new format of corpus is proposed that can be used for training or testing a disambiguation system . |
| Approach: | They propose a format of corpus that can be used for training or testing a disambiguation system . they provide the source code and a complete Java API for manipulating corpora in this format . |
| Outcome: | The proposed format of corpus can be used for training or testing a disambiguation system . the source code and a complete Java API are provided for building the corpus . |
FastSense: An Efficient Word Sense Disambiguation Classifier (L18-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a task that is often overlooked by NLP pipelines because of its complexity and complexity. |
| Approach: | They propose a neural network-based tool for word sense disambiguation called fastSense. |
| Outcome: | The proposed tool can process huge amounts of data quickly and surpasses state-of-the-art tools in terms of F-measure. |
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention (D18-1)
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| Challenge: | Existing models for Word Sense Disambiguation use labeled data, but lack gloss knowledge. |
| Approach: | They propose a co-attention mechanism to generate co-dependent representations for context and gloss . they propose to incorporate gloss knowledge into neural networks for Word Sense Disambiguation . |
| Outcome: | The proposed model achieves state-of-the-art results on standard English all-words WSD datasets. |
Incorporating Glosses into Neural Word Sense Disambiguation (P18-1)
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| Challenge: | Existing neural networks for Word Sense Disambiguation rely on labeled data and lexical knowledge. |
| Approach: | They propose a gloss-augmented WSD neural network which integrates context and glosses of the target word into a unified framework. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on several English all-words WSD datasets. |
Can Word Sense Distribution Detect Semantic Changes of Words? (2023.findings-emnlp)
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| Challenge: | Existing methods to detect semantic variations of words are not accurate for time-sensitive predictions. |
| Approach: | They propose to use pretrained static sense embeddings to annotate a word's occurrence with a sense id to compare its distributions. |
| Outcome: | The proposed method compares word sense distributions across two corpora to predict meaning change . the results show that pretrained LLMs can detect changes in words over time . |
Fine-Grained Event Trigger Detection (2021.eacl-main)
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| Challenge: | Existing methods for Event Detection (ED) focus on a limited set of event types . existing datasets for ED focus on only 33 event types while the number of events in the TAC KBP dataset is 38. |
| Approach: | They propose to transform Word Sense Disambiguation's Semcor dataset into a large and high-quality dataset for ED. |
| Outcome: | The proposed method transforms the Semcor dataset for Word Sense Disambiguation into a large and high-quality dataset for FED. |
Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information (2020.acl-main)
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| Challenge: | Neural architectures are the current state of the art in Word Sense Disambiguation (WSD) however, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKBs). |
| Approach: | They propose a neural supervised architecture that embeds Lexical Knowledge Bases and exploits pretrained synset embeddings to predict synsets that are not in the training set. |
| Outcome: | The proposed architecture breaks through the 80% ceiling on the concatenation of all standard all-words English evaluation benchmarks. |
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing (2022.lrec-1)
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| Challenge: | Existing approaches to understanding textual information are still far from achieving true natural language understanding (NLU). |
| Approach: | They propose a unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantics Parsing. |
| Outcome: | The proposed system can provide users with rich and diverse semantic information, help second-language learners, and integrate explicit semantic knowledge into downstream tasks and real-world applications. |
Don’t Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Existing senseannotated corpora lack coverage of many instances in WordNet . however, unambiguous words make up a large portion of WordNet while being poorly covered in existing senseannnotated . |
| Approach: | They propose a method to provide annotations for most unambiguous words in a large corpus by using a dataset. |
| Outcome: | The proposed method improves on the original results on Word Sense Disambiguation (WSD) using pre-trained language models and propagation algorithms. |
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration (2021.eacl-main)
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| Challenge: | Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem, but gold data suggests that maximizing the probability of a singular sense is not the most suitable training objective for WSD. |
| Approach: | They propose to use Word Sense Disambiguation (WSD) as a multi-label classification problem in which multiple senses can be assigned to each target word. |
| Outcome: | The proposed method bears closer resemblance to how human annotators disambiguate text and can be extended to exploit structured knowledge from semantic networks. |
Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations (2020.coling-main)
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| Challenge: | Word vector representations suffer from a monolingual bias due to the amount of data available across languages. |
| Approach: | They propose a technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. |
| Outcome: | The proposed representations outperform the state-of-the-art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation on low-resource languages. |
Nibbling at the Hard Core of Word Sense Disambiguation (2022.acl-long)
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| Challenge: | Word Sense Disambiguation (WSD) is a task that is based on a set of pre-trained language models. |
| Approach: | They propose to use Word Sense Disambiguation to test whether systems can handle ambiguous words. |
| Outcome: | The proposed benchmarks show that seven of the most representative state-of-the-art systems make trivial errors on traditional evaluation benchmarks. |
ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure (2024.lrec-main)
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| Challenge: | Existing methods for identifying metaphoric expressions in text relied on manual effort to identify the basic and contextual meanings of words. |
| Approach: | They propose a model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. |
| Outcome: | The proposed model outperforms methods that rely on embeddings or integrate only basic definitions and other external knowledge. |
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (D19-1)
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| Challenge: | Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. |
| Approach: | They propose to integrate gloss knowledge into supervised neural networks for Word Sense Disambiguation (WSD) this paper proposes to fine-tune a pre-trained BERT model and achieve new state-of-the-art results on WSD task. |
| Outcome: | The proposed model achieves state-of-the-art on the word Sense Disambiguation (WSD) task. |
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. |
Using Wiktionary to Create Specialized Lexical Resources and Datasets (2022.lrec-1)
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| Challenge: | Using Wiktionary data to build specialized lexical datasets can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation (WSD), Word-in-Context challenges (WiC), or Machine Translation (MT). |
| Approach: | They propose to use Wiktionary data to create specialized lexical datasets that can be used for evaluating or improving NLP tasks. |
| Outcome: | The proposed datasets can be used to improve and/or evaluate NLP tasks, like Word Sense Disambiguation (WSD), Word-in-Context challenges (WiC), or Sense Linking (SL), or machine translation (MT). |
ESC: Redesigning WSD with Extractive Sense Comprehension (2021.naacl-main)
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| Challenge: | Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories. |
| Approach: | They propose a transformer-based neural architecture for extractive Sense Comprehension to solve a span extraction problem and a new state of the art English WSD task. |
| Outcome: | The proposed model outdoes all of its competitors while relying on three times fewer annotations. |
Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives (2021.acl-long)
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| Challenge: | Recent Word Sense Disambiguation systems have approached the upper bound of the task on standard evaluation benchmarks. |
| Approach: | They propose to convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation architecture. |
| Outcome: | The proposed approach surpasses state-of-the-art on English and multilingual datasets by large margins. |
MOSAICo: a Multilingual Open-text Semantically Annotated Interlinked Corpus (2024.naacl-long)
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Simone Conia, Edoardo Barba, Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Riccardo Orlando, Luigi Procopio, Roberto Navigli
| Challenge: | Existing approaches to integrate semantics into Natural Language Understanding (NLP) systems are cost-effective and environmental impact-related. |
| Approach: | They propose to provide semantically-annotated corpora for four NLU tasks across five languages and to drop the requirement of closed datasets. |
| Outcome: | The proposed model provides hundreds of millions of silver yet high-quality annotations for four NLU tasks across five languages. |
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task (2022.lrec-1)
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| Challenge: | Recent research questions whether these models really understand the meaning of natural language. |
| Approach: | They propose to transform the disambiguation of causative-passive homonymy (CPH) to a challenging natural language inference task using a pretrained transformer model RoBERTa. |
| Outcome: | The pretrained model RoBERTa performs poorly on the CANLI dataset . the model's internal representation of CPH is not captured in the model . |
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Existing sense embeddings fail to embed sense knowledge in semantic networks. |
| Approach: | They propose a Synset Relation-Enhanced Framework that leverages sense relations for sense embedding enhancement and a try-again mechanism that implements WSD again. |
| Outcome: | The proposed system outperforms knowledge-based systems with 20% SemCor data on all-words and lexical datasets. |
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. |
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings (P19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing . current methods treat senses as discrete labels and predict the most-frequent-Sense for unseen senses . |
| Approach: | They propose a supervised model to perform Word Sense Disambiguation (WSD) by predicting over a continuous sense embedding space rather than a discrete label space. |
| Outcome: | The proposed model generalizes over seen and unseen senses, achieving zero-shot learning. |
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)
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| Challenge: | Contextual embeddings address the problem of meaning conflation hampering word embeddables. |
| Approach: | They propose a method that creates sense-level embeddings with full-coverage of WordNet without recourse to explicit sense distributions or task-specific modelling. |
| Outcome: | The proposed method surpasses previous systems using powerful models and is robust when ignoring part-of-speech and lemma features. |
Generationary or “How We Went beyond Word Sense Inventories and Learned to Gloss” (2020.emnlp-main)
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| Challenge: | Existing approaches to Word Sense Disambiguation use discrete word senses . however, many language users have different understandings of words . |
| Approach: | They propose a unified computational lexical semantics model that can produce contextually appropriate definitions. |
| Outcome: | The proposed model outperforms existing models in lexical semantics and discriminative tasks. |
Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation (2023.findings-emnlp)
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| Challenge: | Traditional neural network models represent word senses as vectors that are uninterpretable for humans. |
| Approach: | They propose a framework that incorporates word Sense Disambiguation (WSD) by identifying and paraphrasing ambiguous words to improve sentiment predictions. |
| Outcome: | The proposed framework improves sentiment analysis accuracy and interpretability on a downstream task without ground-truth word sense labels. |
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories (2021.emnlp-main)
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| Challenge: | Existing supervised models struggle to make correct predictions on rare word senses due to limited training data. |
| Approach: | They propose a gloss alignment algorithm that can align definition sentences with the same meaning from different sense inventories to collect rich lexical knowledge. |
| Outcome: | The proposed method outperforms previous methods on both frequent and rare word senses. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |
Word Sense Linking: Disambiguating Outside the Sandbox (2024.findings-acl)
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| Challenge: | Word Sense Disambiguation (WSD) systems have performed well on several evaluation benchmarks, but it still struggles to find downstream applications. |
| Approach: | They propose a task where systems have to identify which spans to disambiguate and link them to their most suitable meaning. |
| Outcome: | The proposed task performs above the estimated inter-annotator agreement on a set of words . the proposed system is based on 'transformer-based' architectures and iteratively relaxes the assumptions . |
Language Pivoting from Parallel Corpora for Word Sense Disambiguation of Historical Languages: A Case Study on Latin (2024.lrec-main)
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| Challenge: | Word Sense Disambiguation (WSD) is an important task in NLP . most of the work on this task has been done on contemporary English or other modern languages, leaving challenges posed by low-resource languages and diachronic change open. |
| Approach: | They propose to use existing bilingual corpora instead of native English datasets to generate a Latin WSD model. |
| Outcome: | The proposed approach achieves state-of-the-art on a standard benchmark for Latin WSD. |
Morpheme Sense Disambiguation: A New Task Aiming for Understanding the Language at Character Level (2024.lrec-main)
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| Challenge: | Morphemes are a strong linguistic feature to capture lexical semantics, but lack of morpheme-informed resources and the expense of manual annotations hinder morphme-enhanced methods. |
| Approach: | They propose a task of Morpheme Sense Disambiguation with two subtasks in-text and in-word to generalize morpheme features on more tasks. |
| Outcome: | The proposed tasks are based on two morpheme-annotated datasets for Chinese . the best model yields a promising precision of 77.66% on in-text and 88.19% on in word . |
Strengthening the WiC: New Polysemy Dataset in Hindi and Lack of Cross Lingual Transfer (2024.lrec-main)
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| Challenge: | a new study addresses the problem of natural language processing in low-resource languages such as Hindi . the paper focuses on Word Sense Disambiguation, a fundamental NLP task that deals with polysemous words. |
| Approach: | They propose a Hindi WSD dataset that allows training and testing of contextualized models. |
| Outcome: | The proposed dataset enables training and testing of contextualized models in Hindi . the results show that the proposed dataset can handle polysemy tasks in low-resource languages . |
Do Large Language Models Understand Word Senses? (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have set new performance standards in a wide range of tasks. |
| Approach: | They evaluate the Word Sense Disambiguation capabilities of instruction-tuned LLMs and their ability to understand word senses in three generative settings: definition generation, free-form explanation, and example generation. |
| Outcome: | The proposed models can explain the meaning of words in context with 98% accuracy, while demonstrating greater robustness across domains and levels of difficulty. |