SOTAVerified

Word Sense Disambiguation

The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet.. For example, given the word “mouse” and the following sentence:

“A mouse consists of an object held in one's hand, with one or more buttons.”

we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).

Papers

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Showing 201–250 of 1035 papers

TitleStatusHype
A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features—0
Can Spanish Be Simpler? LexSiS: Lexical Simplification for Spanish—0
Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation—0
A Novel Neural Sequence Model with Multiple Attentions for Word Sense Disambiguation—0
CASE: Context-Aware Semantic Expansion—0
Positional Artefacts Propagate Through Masked Language Model Embeddings—0
Can Crowdsourcing be used for Effective Annotation of Arabic?—0
Anota \~ao de corpus com a OpenWordNet-PT: um exerc\' de desambigua \~ao (Sense annotation with OpenWordNet-PT: an exercise of word sense disambiguation)—0
Chinese Word Sense Disambiguation based on Context Expansion—0
A Joint Sequential and Relational Model for Frame-Semantic Parsing—0
BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings—0
CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of Features—0
Classifying French Verbs Using French and English Lexical Resources—0
Cleaning noisy wordnets—0
Clinical Abbreviation Disambiguation Using Neural Word Embeddings—0
Bulgarian X-language Parallel Corpus—0
Coarse to Fine Grained Sense Disambiguation in Wikipedia—0
Cognate Identification using Machine Translation—0
An Open-source Framework for Multi-level Semantic Similarity Measurement—0
Colors of People (Les couleurs des gens) [in French]—0
Building the Chinese Open Wordnet (COW): Starting from Core Synsets—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Combining POS Tagging, Dependency Parsing and Coreferential Resolution for Bulgarian—0
Combining Relational and Distributional Knowledge for Word Sense Disambiguation—0
Combining resources for MWE-token classification—0
Combining Supervised and Unsupervised Enembles for Knowledge Base Population—0
Building Specialized Bilingual Lexicons Using Word Sense Disambiguation—0
Comparison of Genres in Word Sense Disambiguation using Automatically Generated Text Collections—0
Comparison of Global Algorithms in Word Sense Disambiguation—0
Comparison of the effects of attention mechanism on translation tasks of different lengths of ambiguous words—0
Compression de vocabulaire de sens gr\^ace aux relations s\'emantiques pour la d\'esambigu\" lexicale (Sense Vocabulary Compression through Semantic Knowledge for Word Sense Disambiguation)—0
Concept-based Selectional Preferences and Distributional Representations from Wikipedia Articles—0
Annotation for annotation - Toward eliciting implicit linguistic knowledge through annotation - (Project Note)—0
Connecting people digitally - a semantic web based approach to linking heterogeneous data sets—0
A Java Framework for Multilingual Definition and Hypernym Extraction—0
Building Sense Representations in Danish by Combining Word Embeddings with Lexical Resources—0
Construct a Sense-Frame Aligned Predicate Lexicon for Chinese AMR Corpus—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
Context-Aware In-Page Search—0
Building on Huang et al. GlossBERT for Word Sense Disambiguation—0
Context based Analysis of Lexical Semantics for Hindi Language—0
Context-Dependent Multilingual Lexical Lookup for Under-Resourced Languages—0
Context-Dependent Sense Embedding—0
Context-gloss Augmentation for Improving Word Sense Disambiguation—0
Annotating the MASC Corpus with BabelNet—0
Building a WordNet for Sinhala—0
An Iterative `Sudoku Style' Approach to Subgraph-based Word Sense Disambiguation—0
Coreference Resolution in FreeLing 4.0—0
Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines—0
AI-KU: Using Co-Occurrence Modeling for Semantic Similarity—0
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All datasetsWords in ContextSupervised:RUSSESemEval 2013 Task 12Senseval-2SensEval 3 Task 1SemEval 2007 Task 7SemEval 2007 Task 17FEWSWiC-TSVBIG-bench (Anachronisms)Knowledge-based:

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COSINE + Transductive LearningAccuracy85.3—Unverified
2PaLM 540B (finetuned)Accuracy78.8—Unverified
3ST-MoE-32B 269B (fine-tuned)Accuracy77.7—Unverified
4DeBERTa-EnsembleAccuracy77.5—Unverified
5Vega v2 6B (fine-tuned)Accuracy77.4—Unverified
6UL2 20B (fine-tuned)Accuracy77.3—Unverified
7Turing NLR v5 XXL 5.4B (fine-tuned)Accuracy77.1—Unverified
8T5-XXL 11BAccuracy76.9—Unverified
9DeBERTa-1.5BAccuracy76.4—Unverified
10ST-MoE-L 4.1B (fine-tuned)Accuracy74—Unverified
#ModelMetricClaimedVerifiedStatus
1SANDWiCHSenseval 287.8—Unverified
2GlossGPTSenseval 286.1—Unverified
3ConSeC+WNGCSenseval 282.7—Unverified
4ESR+WNGCSenseval 282.5—Unverified
5ConSeCSenseval 282.3—Unverified
6ESCHER SemCorSenseval 281.7—Unverified
7ESRSenseval 281.3—Unverified
8EWISER+WNGCSenseval 280.8—Unverified
9SemCor+WNGC, hypernymsSenseval 279.7—Unverified
10SparseLMMS+WNGCSenseval 279.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Human BenchmarkAccuracy0.81—Unverified
2ruT5-large-finetuneAccuracy0.74—Unverified
3RuBERT conversationalAccuracy0.73—Unverified
4RuBERT plainAccuracy0.73—Unverified
5ruRoberta-large finetuneAccuracy0.72—Unverified
6ruBert-base finetuneAccuracy0.71—Unverified
7Multilingual BertAccuracy0.69—Unverified
8ruT5-base-finetuneAccuracy0.68—Unverified
9ruBert-large finetuneAccuracy0.68—Unverified
10SBERT_Large_mt_ru_finetuningAccuracy0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF178.7—Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF172.63—Unverified
3LSTMLP (T:SemCor, U:1K)F169.5—Unverified
4LSTMLP (T:OMSTI, U:1K)F168.1—Unverified
5LSTMLP (T:SemCor, U:OMSTI)F167.9—Unverified
6LSTM (T:OMSTI)F167.3—Unverified
7GASext (Concatenation)F167.2—Unverified
8GASext (Linear)F167.1—Unverified
9GAS (Concatenation)F167—Unverified
10LSTM (T:SemCor)F167—Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF179.7—Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF175.15—Unverified
3LSTMLP (T:OMSTI, U:1K)F174.4—Unverified
4LSTMLP (T:SemCor, U:OMSTI)F173.9—Unverified
5LSTMLP (T:SemCor, U:1K)F173.8—Unverified
6LSTM (T:SemCor)F173.6—Unverified
7GASext (Linear)F172.4—Unverified
8LSTM (T:OMSTI)F172.4—Unverified
9GASext (Concatenation)F172.2—Unverified
10GAS (Concatenation)F172.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF177.8—Unverified
2LSTMLP (T:SemCor, U:1K)F171.8—Unverified
3LSTMLP (T:SemCor, U:OMSTI)F171.1—Unverified
4LSTMLP (T:OMSTI, U:1K)F171—Unverified
5GASext (Concatenation)F170.5—Unverified
6GAS (Concatenation)F170.2—Unverified
7SemCor+WNGT, vocabulary reduced, ensembleF170.11—Unverified
8GASext (Linear)F170.1—Unverified
9GAS (Linear)F170—Unverified
10LSTM (T:SemCor)F169.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF190.4—Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF186.02—Unverified
3kNN-BERT + POS (training corpus: WNGT)F185.32—Unverified
4LSTMLP (T:SemCor, U:OMSTI)F184.3—Unverified
5LSTMLP (T:SemCor, U:1K)F183.6—Unverified
6LSTMLP (T:OMSTI, U:1K)F183.3—Unverified
7LSTM (T:SemCor)F182.8—Unverified
8ShotgunWSD 2.0F181.22—Unverified
9kNN-BERTF181.2—Unverified
10LSTM (T:OMSTI)F181.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF173.4—Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF166.81—Unverified
3LSTM (T:SemCor)F164.2—Unverified
4LSTMLP (T:SemCor, U:OMSTI)F163.7—Unverified
5LSTMLP (T:SemCor, U:1K)F163.5—Unverified
6LSTMLP (T:OMSTI, U:1K)F163.3—Unverified
7kNN-BERT + POS (training corpus: SemCor)F163.17—Unverified
8kNN-BERTF160.94—Unverified
9LSTM (T:OMSTI)F160.7—Unverified
#ModelMetricClaimedVerifiedStatus
1GlossGPTF1 (Zeroshot Dev)81.8—Unverified
2ESR LargeF1 (Zeroshot Dev)77.4—Unverified
3ESR baseF1 (Zeroshot Dev)73.9—Unverified
4SEMEq LargeF1 (Zeroshot Dev)73.7—Unverified
5SEMeq baseF1 (Zeroshot Dev)71.5—Unverified
6RTWE largeF1 (Zero shot test)69.9—Unverified
7LeskF1 (Zeroshot Dev)40.1—Unverified
8MFSF1 (Zeroshot Dev)0—Unverified
#ModelMetricClaimedVerifiedStatus
1HumanTask 3 Accuracy: all85.3—Unverified
2transformersTask 1 Accuracy: all77.8—Unverified
3CTLRTask 1 Accuracy: all76.8—Unverified
4GlossBert-wsTask 1 Accuracy: all75.9—Unverified
5Bert-baseTask 1 Accuracy: all75.3—Unverified
6Unsupervised BertTask 1 Accuracy: all54.4—Unverified
7FastTextTask 1 Accuracy: all53.7—Unverified
8All trueTask 1 Accuracy: all50.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy69.1—Unverified
2Gopher-280B (few-shot, k=5)Accuracy56.4—Unverified
3OPT 175BAccuracy49.1—Unverified
4GAL 120B (few-shot, k=5)Accuracy48.7—Unverified
5GAL 30B (few-shot, k=5)Accuracy47—Unverified
6BLOOM 176BAccuracy1.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UKBppr_w2wSenseval 268.8—Unverified
2KEFAll68—Unverified
3WSD-TMAll66.9—Unverified
4BabelfyAll65.5—Unverified
5WN 1st sense baselineAll65.2—Unverified
6UKBppr_w2w-nfAll57.5—Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF182.6—Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF174.46—Unverified
3GASext (Concatenation)F172.6—Unverified
4GASext (Linear)F172.1—Unverified
5GAS (Concatenation)F171.8—Unverified
6GAS (Linear)F171.6—Unverified
#ModelMetricClaimedVerifiedStatus
1kNN-BERTF180.12—Unverified
2IMS + adapted CWF173.4—Unverified
3BiLSTM with GloVeF173.4—Unverified
4Single BiLSTMF172.5—Unverified
#ModelMetricClaimedVerifiedStatus
1kNN-BERTF176.52—Unverified
2BiLSTM with GloVeF166.9—Unverified
3IMS + adapted CWF166.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SPINSequence Recovery %(All)30.3—Unverified