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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 391–400 of 1564 papers

Title	Date	Tasks	Status	Hype
Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method	Jan 6, 2024	Patent classificationSemantic Similarity	—Unverified	0
PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging	Jan 5, 2024	Medical Report GenerationMedical Visual Question Answering	CodeCode Available	2
Saliency-Aware Regularized Graph Neural Network	Jan 1, 2024	ClassificationGraph Classification	—Unverified	0
Are we describing the same sound? An analysis of word embedding spaces of expressive piano performance	Dec 31, 2023	Information RetrievalRetrieval	CodeCode Available	0
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining	Dec 25, 2023	Domain AdaptationLanguage Modelling	—Unverified	0
MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning	Dec 22, 2023	Prompt LearningSemantic Similarity	—Unverified	0
An Empirical study of Unsupervised Neural Machine Translation: analyzing NMT output, model's behavior and sentences' contribution	Dec 19, 2023	Low Resource Neural Machine TranslationLow-Resource Neural Machine Translation	—Unverified	0
Def2Vec: Extensible Word Embeddings from Dictionary Definitions	Dec 16, 2023	Chunkingnamed-entity-recognition	CodeCode Available	0
Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models	Dec 15, 2023	BenchmarkingCode Summarization	CodeCode Available	1
Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach	Dec 15, 2023	PredictionRetrieval	CodeCode Available	1

Show:10 25 50

← PrevPage 40 of 157Next →

All datasets Annotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICK BIOSSES CHIP-STS ClinicalSTS MedSTS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	93.38	—	Unverified
2	SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	91.51	—	Unverified
3	SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	90.69	—	Unverified
4	BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	89.16	—	Unverified
5	BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	89.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.75	—	Unverified
2	SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
3	SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
4	BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	86.8	—	Unverified
5	BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	84.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Doc2VecC	MSE	0.31	—	Unverified
2	LSTM (Tai et al., 2015)	MSE	0.28	—	Unverified
3	Bidirectional LSTM (Tai et al., 2015)	MSE	0.27	—	Unverified
4	combine-skip (Kiros et al., 2015)	MSE	0.27	—	Unverified
5	Dependency Tree-LSTM (Tai et al., 2015)	MSE	0.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BioLinkBERT (large)	Pearson Correlation	0.94	—	Unverified
2	BioLinkBERT (base)	Pearson Correlation	0.93	—	Unverified
3	NCBI_BERT(base) (P+M)	Pearson Correlation	0.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MacBERT-large	Macro F1	85.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CharacterBERT (base, medical, ensemble)	Pearson Correlation	85.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	NCBI_BERT(base) (P+M)	Pearson Correlation	0.85	—	Unverified