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

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Showing 1441–1450 of 1564 papers

Title	Date	Tasks	Status
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks	May 8, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
Typology of Adjectives Benchmark for Compositional Distributional Models	May 1, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
Extending Monolingual Semantic Textual Similarity Task to Multiple Cross-lingual Settings	May 1, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
SemAligner: A Method and Tool for Aligning Chunks with Semantic Relation Types and Semantic Similarity Scores	May 1, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
A sense-based lexicon of count and mass expressions: The Bochum English Countability Lexicon	May 1, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
From Interoperable Annotations towards Interoperable Resources: A Multilingual Approach to the Analysis of Discourse	May 1, 2016	Machine TranslationSemantic Similarity	—Unverified
What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis	May 1, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
Evaluating Lexical Similarity to build Sentiment Similarity	May 1, 2016	Information RetrievalRetrieval	—Unverified
Cognitively Motivated Distributional Representations of Meaning	May 1, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
Image Colorization Using a Deep Convolutional Neural Network	Apr 27, 2016	ColorizationGeneral Classification	—Unverified

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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