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 601–610 of 1564 papers

Title	Date	Tasks	Status	Hype
Bridging Fairness and Environmental Sustainability in Natural Language Processing	Nov 8, 2022	Dimensionality ReductionFairness	—Unverified	0
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables	Nov 7, 2022	Language ModelingLanguage Modelling	CodeCode Available	0
H_eval: A new hybrid evaluation metric for automatic speech recognition tasks	Nov 3, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question	Nov 2, 2022	Question AnsweringQuestion Generation	CodeCode Available	1
Unsupervised Text Summarization of Long Documents using Dependency-based Noun Phrases and Contextual Order Arrangement	Nov 1, 2022	Extractive SummarizationSemantic Similarity	—Unverified	0
Sentiment Classification of Code-Switched Text using Pre-trained Multilingual Embeddings and Segmentation	Oct 29, 2022	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Zero-Shot Text Matching for Automated Auditing using Sentence Transformers	Oct 28, 2022	Information RetrievalQuestion Answering	—Unverified	0
You can't pick your neighbors, or can you? When and how to rely on retrieval in the kNN-LM	Oct 28, 2022	Language ModelingLanguage Modelling	CodeCode Available	0
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model	Oct 27, 2022	Language ModelingLanguage Modelling	—Unverified	0
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation	Oct 27, 2022	Image GenerationSemantic Similarity	CodeCode Available	0

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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