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

Showing 451500 of 1035 papers

TitleStatusHype
Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection0
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation0
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison0
Multilingual CALL Framework for Automatic Language Exercise Generation from Free Text0
Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique0
A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features0
Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language Model0
Exploring Soft-Clustering for German (Particle) Verbs across Frequency Ranges0
Sense Embeddings in Knowledge-Based Word Sense DisambiguationCode0
Dual Embeddings and Metrics for Relational Similarity0
Distributional Lesk: Effective Knowledge-Based Word Sense Disambiguation0
Regular polysemy: from sense vectors to sense patterns0
A Bayesian model for joint word alignment and part-of-speech transfer0
Integrating WordNet for Multiple Sense Embeddings in Vector Semantics0
Incremental Global Event Extraction0
Improving Translation Selection with Supersenses0
Improving Statistical Machine Translation with Selectional Preferences0
Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen0
Verb sense disambiguation in Machine Translation0
Structured Generative Models of Continuous Features for Word Sense Induction0
Featureless Domain-Specific Term Extraction with Minimal Labelled Data0
Feature based Sentiment Analysis using a Domain Ontology0
Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning0
Extending AIDA framework by incorporating coreference resolution on detected mentions and pruning based on popularity of an entity0
Zooming in on Gender Differences in Social Media0
papago: A Machine Translation Service with Word Sense Disambiguation and Currency Conversion0
Expanding wordnets to new languages with multilingual sense disambiguation0
Semantically Motivated Hebrew Verb-Noun Multi-Word Expressions Identification0
Sinhala Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures0
More is not always better: balancing sense distributions for all-words Word Sense DisambiguationCode0
A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation0
Jointly Embedding Knowledge Graphs and Logical Rules0
Context-Dependent Sense Embedding0
Combining Supervised and Unsupervised Enembles for Knowledge Base Population0
Supervised Distributional Hypernym Discovery via Domain Adaptation0
Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings0
EmojiNet: Building a Machine Readable Sense Inventory for Emoji0
A Hybrid Approach for Deep Machine Translation0
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings0
Towards Accurate and Efficient Chinese Part-of-Speech Tagging0
Using Distributed Representations to Disambiguate Biomedical and Clinical ConceptsCode0
Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM0
Learning Paraphrasing for Multiword Expressions0
Improving Text-to-Pictograph Translation Through Word Sense Disambiguation0
LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations0
An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods0
Language Transfer Learning for Supervised Lexical Substitution0
Accounting ngrams and multi-word terms can improve topic models0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COSINE + Transductive LearningAccuracy85.3Unverified
2PaLM 540B (finetuned)Accuracy78.8Unverified
3ST-MoE-32B 269B (fine-tuned)Accuracy77.7Unverified
4DeBERTa-EnsembleAccuracy77.5Unverified
5Vega v2 6B (fine-tuned)Accuracy77.4Unverified
6UL2 20B (fine-tuned)Accuracy77.3Unverified
7Turing NLR v5 XXL 5.4B (fine-tuned)Accuracy77.1Unverified
8T5-XXL 11BAccuracy76.9Unverified
9DeBERTa-1.5BAccuracy76.4Unverified
10ST-MoE-L 4.1B (fine-tuned)Accuracy74Unverified
#ModelMetricClaimedVerifiedStatus
1SANDWiCHSenseval 287.8Unverified
2GlossGPTSenseval 286.1Unverified
3ConSeC+WNGCSenseval 282.7Unverified
4ESR+WNGCSenseval 282.5Unverified
5ConSeCSenseval 282.3Unverified
6ESCHER SemCorSenseval 281.7Unverified
7ESRSenseval 281.3Unverified
8EWISER+WNGCSenseval 280.8Unverified
9SemCor+WNGC, hypernymsSenseval 279.7Unverified
10SparseLMMS+WNGCSenseval 279.6Unverified
#ModelMetricClaimedVerifiedStatus
1Human BenchmarkAccuracy0.81Unverified
2ruT5-large-finetuneAccuracy0.74Unverified
3RuBERT conversationalAccuracy0.73Unverified
4RuBERT plainAccuracy0.73Unverified
5ruRoberta-large finetuneAccuracy0.72Unverified
6ruBert-base finetuneAccuracy0.71Unverified
7Multilingual BertAccuracy0.69Unverified
8ruT5-base-finetuneAccuracy0.68Unverified
9ruBert-large finetuneAccuracy0.68Unverified
10SBERT_Large_mt_ru_finetuningAccuracy0.66Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF178.7Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF172.63Unverified
3LSTMLP (T:SemCor, U:1K)F169.5Unverified
4LSTMLP (T:OMSTI, U:1K)F168.1Unverified
5LSTMLP (T:SemCor, U:OMSTI)F167.9Unverified
6LSTM (T:OMSTI)F167.3Unverified
7GASext (Concatenation)F167.2Unverified
8GASext (Linear)F167.1Unverified
9GAS (Concatenation)F167Unverified
10LSTM (T:SemCor)F167Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF179.7Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF175.15Unverified
3LSTMLP (T:OMSTI, U:1K)F174.4Unverified
4LSTMLP (T:SemCor, U:OMSTI)F173.9Unverified
5LSTMLP (T:SemCor, U:1K)F173.8Unverified
6LSTM (T:SemCor)F173.6Unverified
7GASext (Linear)F172.4Unverified
8LSTM (T:OMSTI)F172.4Unverified
9GASext (Concatenation)F172.2Unverified
10GAS (Concatenation)F172.1Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF177.8Unverified
2LSTMLP (T:SemCor, U:1K)F171.8Unverified
3LSTMLP (T:SemCor, U:OMSTI)F171.1Unverified
4LSTMLP (T:OMSTI, U:1K)F171Unverified
5GASext (Concatenation)F170.5Unverified
6GAS (Concatenation)F170.2Unverified
7SemCor+WNGT, vocabulary reduced, ensembleF170.11Unverified
8GASext (Linear)F170.1Unverified
9GAS (Linear)F170Unverified
10LSTM (T:SemCor)F169.2Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF190.4Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF186.02Unverified
3kNN-BERT + POS (training corpus: WNGT)F185.32Unverified
4LSTMLP (T:SemCor, U:OMSTI)F184.3Unverified
5LSTMLP (T:SemCor, U:1K)F183.6Unverified
6LSTMLP (T:OMSTI, U:1K)F183.3Unverified
7LSTM (T:SemCor)F182.8Unverified
8ShotgunWSD 2.0F181.22Unverified
9kNN-BERTF181.2Unverified
10LSTM (T:OMSTI)F181.1Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF173.4Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF166.81Unverified
3LSTM (T:SemCor)F164.2Unverified
4LSTMLP (T:SemCor, U:OMSTI)F163.7Unverified
5LSTMLP (T:SemCor, U:1K)F163.5Unverified
6LSTMLP (T:OMSTI, U:1K)F163.3Unverified
7kNN-BERT + POS (training corpus: SemCor)F163.17Unverified
8kNN-BERTF160.94Unverified
9LSTM (T:OMSTI)F160.7Unverified
#ModelMetricClaimedVerifiedStatus
1GlossGPTF1 (Zeroshot Dev)81.8Unverified
2ESR LargeF1 (Zeroshot Dev)77.4Unverified
3ESR baseF1 (Zeroshot Dev)73.9Unverified
4SEMEq LargeF1 (Zeroshot Dev)73.7Unverified
5SEMeq baseF1 (Zeroshot Dev)71.5Unverified
6RTWE largeF1 (Zero shot test)69.9Unverified
7LeskF1 (Zeroshot Dev)40.1Unverified
8MFSF1 (Zeroshot Dev)0Unverified
#ModelMetricClaimedVerifiedStatus
1HumanTask 3 Accuracy: all85.3Unverified
2transformersTask 1 Accuracy: all77.8Unverified
3CTLRTask 1 Accuracy: all76.8Unverified
4GlossBert-wsTask 1 Accuracy: all75.9Unverified
5Bert-baseTask 1 Accuracy: all75.3Unverified
6Unsupervised BertTask 1 Accuracy: all54.4Unverified
7FastTextTask 1 Accuracy: all53.7Unverified
8All trueTask 1 Accuracy: all50.8Unverified
#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy69.1Unverified
2Gopher-280B (few-shot, k=5)Accuracy56.4Unverified
3OPT 175BAccuracy49.1Unverified
4GAL 120B (few-shot, k=5)Accuracy48.7Unverified
5GAL 30B (few-shot, k=5)Accuracy47Unverified
6BLOOM 176BAccuracy1.3Unverified
#ModelMetricClaimedVerifiedStatus
1UKBppr_w2wSenseval 268.8Unverified
2KEFAll68Unverified
3WSD-TMAll66.9Unverified
4BabelfyAll65.5Unverified
5WN 1st sense baselineAll65.2Unverified
6UKBppr_w2w-nfAll57.5Unverified
#ModelMetricClaimedVerifiedStatus
1SemCor+WNGC, hypernymsF182.6Unverified
2SemCor+WNGT, vocabulary reduced, ensembleF174.46Unverified
3GASext (Concatenation)F172.6Unverified
4GASext (Linear)F172.1Unverified
5GAS (Concatenation)F171.8Unverified
6GAS (Linear)F171.6Unverified
#ModelMetricClaimedVerifiedStatus
1kNN-BERTF180.12Unverified
2IMS + adapted CWF173.4Unverified
3BiLSTM with GloVeF173.4Unverified
4Single BiLSTMF172.5Unverified
#ModelMetricClaimedVerifiedStatus
1kNN-BERTF176.52Unverified
2BiLSTM with GloVeF166.9Unverified
3IMS + adapted CWF166.2Unverified
#ModelMetricClaimedVerifiedStatus
1SPINSequence Recovery %(All)30.3Unverified