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
Most implemented papers
Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch.
To Tune or Not To Tune? How About the Best of Both Worlds?
uzaymacar/comparatively-finetuning-bert
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In this regard, Peters et al. perform several experiments which demonstrate that it is better to adapt BERT with a light-weight task-specific head, rather than building a complex one on top of the pre-trained language model, and freeze the parameters in the said language model.
A Hybrid Neural Network Model for Commonsense Reasoning
An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERT-based contextual encoder but use different model-specific input and output layers.
Semantic Relatedness Based Re-ranker for Text Spotting
ahmedssabir/Semantic-Relatedness-Based-Reranker-for-Text-Spotting
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IJCNLP 2019
We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness.
On the Sentence Embeddings from Pre-trained Language Models
bohanli/BERT-flow
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EMNLP 2020
Pre-trained contextual representations like BERT have achieved great success in natural language processing.
Generating Natural Language Attacks in a Hard Label Black Box Setting
RishabhMaheshwary/hard-label-attack
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Our proposed attack strategy leverages population-based optimization algorithm to craft plausible and semantically similar adversarial examples by observing only the top label predicted by the target model.
On Semantic Similarity in Video Retrieval
Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa.
Using Information Content to Evaluate Semantic Similarity in a Taxonomy
This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content.
Counter-fitting Word Vectors to Linguistic Constraints
In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.
No Fuss Distance Metric Learning using Proxies
dichotomies/proxy-nca
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ICCV 2017
Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized.
