Semantic Similarity
417 papers with code • 8 benchmarks • 12 datasets
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
Benchmarks
These leaderboards are used to track progress in Semantic Similarity
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Libraries
Use these libraries to find Semantic Similarity models and implementationsDatasets
SICK
STS Benchmark
BIOSSES
SV-Ident
Latest papers
A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances
While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity.
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
hhc1997/l2rm
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To achieve this, we propose L2RM, a general framework based on Optimal Transport (OT) that learns to rematch mismatched pairs.
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
infzhou/sam-pd
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Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images.
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies.
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations
When deriving contextualized word representations from language models, a decision needs to be made on how to obtain one for out-of-vocabulary (OOV) words that are segmented into subwords.
UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with and without machine translation
The aim of SemEval-2024 Task 1, "Semantic Textual Relatedness for African and Asian Languages" is to develop models for identifying semantic textual relatedness (STR) between two sentences using multiple languages (14 African and Asian languages) and settings (supervised, unsupervised, and cross-lingual).
Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric
Medical language processing and deep learning techniques have emerged as critical tools for improving healthcare, particularly in the analysis of medical imaging and medical text data.
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
semantic-textual-relatedness/semantic_relatedness_semeval2024
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Exploring and quantifying semantic relatedness is central to representing language.
OrderBkd: Textual backdoor attack through repositioning
The use of third-party datasets and pre-trained machine learning models poses a threat to NLP systems due to possibility of hidden backdoor attacks.
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
hqa-attack/hqaattack-demo
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NeurIPS 2023
Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.
