SOTAVerified

Semantic Similarity

The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.

Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1350 of 1564 papers

TitleStatusHype
SSAS: Semantic Similarity for Abstractive Summarization—0
The Challenge of Composition in Distributional and Formal Semantics—0
Testing the limits of unsupervised learning for semantic similarity—0
A Semantically Motivated Approach to Compute ROUGE Scores—0
Specialising Word Vectors for Lexical EntailmentCode0
Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer—0
A Semantic Relevance Based Neural Network for Text Summarization and Text SimplificationCode0
An\'alise de Medidas de Similaridade Sem\^antica na Tarefa de Reconhecimento de Implica \~ao Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In Portuguese]—0
An enhanced method to compute the similarity between concepts of ontology—0
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness—0
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]—0
Think Globally, Embed Locally --- Locally Linear Meta-embedding of WordsCode0
Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 2016—0
Czech Dataset for Semantic Similarity and Relatedness—0
Parameter Transfer across Domains for Word Sense Disambiguation—0
The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings—0
Distractor Generation for Chinese Fill-in-the-blank Items—0
Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation—0
Latent Space Embedding for Retrieval in Question-Answer Archives—0
Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews—0
The strange geometry of skip-gram with negative sampling—0
Identifying Cognate Sets Across Dictionaries of Related LanguagesCode0
Variational Inference for Logical Inference—0
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language TasksCode0
ClaC: Semantic Relatedness of Words and Phrases—0
Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data—0
Evaluating text coherence based on semantic similarity graph—0
A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity—0
Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity—0
HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity—0
Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarityCode0
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity—0
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity—0
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise FeaturesCode0
SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector—0
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting—0
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity—0
ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity—0
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA—0
Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection—0
SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering—0
DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output—0
LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity—0
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity—0
Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity—0
MITRE at SemEval-2017 Task 1: Simple Semantic Similarity—0
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All datasetsAnnotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICKBIOSSESCHIP-STSClinicalSTSMedSTS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F193.38—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F191.51—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F190.69—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.16—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.12—Unverified
#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.75—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F186.8—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F184.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Doc2VecCMSE0.31—Unverified
2LSTM (Tai et al., 2015)MSE0.28—Unverified
3Bidirectional LSTM (Tai et al., 2015)MSE0.27—Unverified
4combine-skip (Kiros et al., 2015)MSE0.27—Unverified
5Dependency Tree-LSTM (Tai et al., 2015)MSE0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BioLinkBERT (large)Pearson Correlation0.94—Unverified
2BioLinkBERT (base)Pearson Correlation0.93—Unverified
3NCBI_BERT(base) (P+M)Pearson Correlation0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MacBERT-largeMacro F185.6—Unverified
#ModelMetricClaimedVerifiedStatus
1CharacterBERT (base, medical, ensemble)Pearson Correlation85.62—Unverified
#ModelMetricClaimedVerifiedStatus
1NCBI_BERT(base) (P+M)Pearson Correlation0.85—Unverified