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 701–750 of 1035 papers

TitleStatusHype
SWIFT Aligner, A Multifunctional Tool for Parallel Corpora: Visualization, Word Alignment, and (Morpho)-Syntactic Cross-Language Transfer—0
Synonymy in Bilingual Context: The CzEngClass Lexicon—0
Synset Ranking of Hindi WordNet—0
Syntactically Aware Neural Architectures for Definition Extraction—0
Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining—0
SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations—0
Syntax-Semantics Interaction Parsing Strategies. Inside SYNTAGMA—0
SzegedAI at SemEval-2021 Task 2: Zero-shot Approach for Multilingual and Cross-lingual Word-in-Context Disambiguation—0
TakeLab: Systems for Measuring Semantic Text Similarity—0
TeamUFAL: WSD+EL as Document Retrieval—0
TeamZ: Measuring Semantic Textual Similarity for Spanish Using an Overlap-Based Approach—0
Temporal Word Meaning Disambiguation using TimeLMs—0
Text Understanding using Knowledge-Bases and Random Walks—0
That's sick dude!: Automatic identification of word sense change across different timescales—0
The Chinese Causative-Passive Homonymy Disambiguation: an adversarial Dataset for NLI and a Probing Task—0
The Effects of Lexical Resource Quality on Preference Violation Detection—0
The Extended Arabic WordNet: a Case Study and an Evaluation using a Word Sense Disambiguation System—0
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis—0
The Making of Ancient Greek WordNet—0
The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages—0
The Polish Sejm Corpus—0
The Procedure of Lexico-Semantic Annotation of Sk Treebank—0
The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD—0
The Role of the WordNet Relations in the Knowledge-based Word Sense Disambiguation Task—0
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources—0
The (too Many) Problems of Analogical Reasoning with Word Vectors—0
The Topology of Semantic Knowledge—0
The WoDiS System - WOlf and DIStributions for Lexical Substitution (Le syst\`eme WoDiS - WOLF et DIStributions pour la substitution lexicale) [in French]—0
The Word Sense Disambiguation Test Suite at WMT18—0
Threat detection in online discussions—0
Three Knowledge-Free Methods for Automatic Lexical Chain Extraction—0
Tiantianzhu7:System Description of Semantic Textual Similarity (STS) in the SemEval-2012 (Task 6)—0
To Exhibit is not to Loiter: A Multilingual, Sense-Disambiguated Wiktionary for Measuring Verb Similarity—0
To lemmatize or not to lemmatize: how word normalisation affects ELMo performance in word sense disambiguation—0
Tone Restoration in Transcribed Kammu: Decision-List Word Sense Disambiguation for an Unwritten Language—0
Topic Level Disambiguation for Weak Queries—0
Topic Models: Accounting Component Structure of Bigrams—0
Topological Data Analysis for Word Sense Disambiguation—0
Toshiba MT System Description for the WAT2014 Workshop—0
Toshiba MT System Description for the WAT2015 Workshop—0
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE—0
Towards Accurate and Efficient Chinese Part-of-Speech Tagging—0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language—0
Towards Building a Multilingual Semantic Network: Identifying Interlingual Links in Wikipedia—0
Towards Building KurdNet, the Kurdish WordNet—0
Towards Dynamic Word Sense Discrimination with Random Indexing—0
Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation—0
Towards Human Evaluation of Mutual Understanding in Human-Computer Spontaneous Conversation: An Empirical Study of Word Sense Disambiguation for Naturalistic Social Dialogs in American English—0
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings—0
Towards the automatic classification of complex-type nominals—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