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 951–1000 of 1035 papers

TitleStatusHype
SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity—0
Tiantianzhu7:System Description of Semantic Textual Similarity (STS) in the SemEval-2012 (Task 6)—0
SemEval-2012 Task 5: Chinese Semantic Dependency Parsing—0
TakeLab: Systems for Measuring Semantic Text Similarity—0
UMCC\_DLSI: Multidimensional Lexical-Semantic Textual Similarity—0
MIXCD: System Description for Evaluating Chinese Word Similarity at SemEval-2012—0
Weiwei: A Simple Unsupervised Latent Semantics based Approach for Sentence Similarity—0
UNT: A Supervised Synergistic Approach to Semantic Text Similarity—0
Extracting a Semantic Lexicon of French Adjectives from a Large Lexicographic Dictionary—0
Unsupervised Disambiguation of Image Captions—0
An Evaluation of Graded Sense Disambiguation using Word Sense Induction—0
Combining resources for MWE-token classification—0
Towards Building a Multilingual Semantic Network: Identifying Interlingual Links in Wikipedia—0
Regular polysemy: A distributional model—0
Multilingual WSD with Just a Few Lines of Code: the BabelNet API—0
Evaluating Unsupervised Ensembles when applied to Word Sense Induction—0
A Broad Evaluation of Techniques for Automatic Acquisition of Multiword Expressions—0
Learning the Latent Semantics of a Concept from its Definition—0
Classifying French Verbs Using French and English Lexical Resources—0
Capturing Paradigmatic and Syntagmatic Lexical Relations: Towards Accurate Chinese Part-of-Speech Tagging—0
Word Sense Disambiguation Improves Information Retrieval—0
\'Etat de l'art : mesures de similarit\'e s\'emantique locales et algorithmes globaux pour la d\'esambigu\" lexicale \`a base de connaissances (State of the art : Local Semantic Similarity Measures and Global Algorithmes for Knowledge-based Word Sense Disambiguation) [in French]—0
A Study of Heterogeneous Similarity Measures for Semantic Relation Extraction—0
Adding Distributional Semantics to Knowledge Base Entities through Web-scale Entity Linking—0
LIMSI @ WMT12—0
Scaling up WSD with Automatically Generated Examples—0
Sense-Specific Lexical Information for Reading Assistance—0
Measuring Word Relatedness Using Heterogeneous Vector Space Models—0
Using Senses in HMM Word Alignment—0
Encouraging Consistent Translation Choices—0
Expectations of Word Sense in Parallel Corpora—0
Mining wisdom—0
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language ModelCode0
Assigning Connotation Values to Events—0
KPWr: Towards a Free Corpus of Polish—0
Wordnet extension made simple: A multilingual lexicon-based approach using wiki resources—0
Unsupervised Word Sense Disambiguation with Multilingual Representations—0
Mapping WordNet to the Kyoto ontology—0
Buildind a Resource of Patterns Using Semantic Types—0
Word Sense Inventories by Non-Experts.—0
Linguagrid: a network of Linguistic and Semantic Services for the Italian Language.—0
Evaluation of Classification Algorithms and Features for Collocation Extraction in Croatian—0
Cleaning noisy wordnets—0
Mapping WordNet synsets to Wikipedia articles—0
SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis—0
Is it Useful to Support Users with Lexical Resources? A User Study.—0
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples—0
Evaluating the Impact of Phrase Recognition on Concept Tagging—0
Discovering Missing Wikipedia Inter-language Links by means of Cross-lingual Word Sense Disambiguation—0
A new semantically annotated corpus with syntactic-semantic and cross-lingual senses—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