Semantic Similarity
418 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
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources.
A Semantics-Based Measure of Emoji Similarity
joonasrooben/NLP-text2emoji
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This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base.
Ad Hoc Table Retrieval using Semantic Similarity
Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations.
Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing
Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems.
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
antoine77340/howto100m
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ECCV 2018
We present an approach named JSFusion (Joint Sequence Fusion) that can measure semantic similarity between any pairs of multimodal sequence data (e. g. a video clip and a language sentence).
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content
WeimingWen/CCRV
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EMNLP 2018
With the increasing popularity of smart devices, rumors with multimedia content become more and more common on social networks.
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
abaheti95/DC-NeuralConversation
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EMNLP 2018
Neural conversation models tend to generate safe, generic responses for most inputs.
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings
Monolingual dictionaries are widespread and semantically rich resources.
Ranked List Loss for Deep Metric Learning
To address this, we propose to build a set-based similarity structure by exploiting all instances in the gallery.
A Bilingual Generative Transformer for Semantic Sentence Embedding
jwieting/bilingual-generative-transformer
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EMNLP 2020
Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences.
