Papers with Code 아카이브

Semantic Similarity

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 2
평가표: 28
보존된 평가 기록: 21

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/gg42554/cl-sts

공식 코드

A Resource-Light Method for Cross-Lingual Semantic Textual Similarity

https://git.tu-berlin.de/rsim/image-retrieval-from-triplets

공식 코드

Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval

https://github.com/0xfafa/fa

공식 코드

FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection

https://github.com/4ai/bellm

공식 코드

BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings

https://github.com/8023looker/datasculpt

공식 코드

DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning

https://github.com/AI-Growth-Lab/Patent-Classification

공식 코드

PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT

https://github.com/AIPHES/ACL20-Reference-Free-MT-Evaluation

공식 코드

On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation

https://github.com/Adamkubany/Multilabel_Semantic_API_comparison

공식 코드

Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic Metrics

https://github.com/AhilanPonnusamy/Reverse-Generation-and-SBC-Metric--Reference-Implementation

공식 코드

Bridging LLM-Generated Code and Requirements: Reverse Generation technique and SBC Metric for Developer Insights

https://github.com/AliNajafi1998/ComStream

공식 코드

ComStreamClust: a communicative multi-agent approach to text clustering in streaming data

https://github.com/Babylonpartners/MCSG

공식 코드

Model Comparison for Semantic Grouping

https://github.com/BatsResearch/efsl

공식 코드

Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks

https://github.com/Bill2462/prompt-map

공식 코드

PromptMap: An Alternative Interaction Style for AI-Based Image Generation

https://github.com/Bollegala/jointreps

공식 코드

Joint Word Representation Learning using a Corpus and a Semantic Lexicon

https://github.com/CUHK-ARISE/AEON

공식 코드

AEON: A Method for Automatic Evaluation of NLP Test Cases

https://github.com/DAI-Lab/fibber

공식 코드

R&R: Metric-guided Adversarial Sentence Generation

https://github.com/DRSY/KV_Compression

공식 코드

Context Compression for Auto-regressive Transformers with Sentinel Tokens

https://github.com/DannyMerkx/caption2image

공식 코드

Learning semantic sentence representations from visually grounded language without lexical knowledge

https://github.com/DannyMerkx/speech2image

공식 코드

Semantic sentence similarity: size does not always matter

https://github.com/DeepLearnXMU/BattRAE

공식 코드

BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings

https://github.com/EvLab-MIT/LexiContrastiveGrd

공식 코드

Visual Grounding Helps Learn Word Meanings in Low-Data Regimes

https://github.com/Extreme-classification/DECAF

공식 코드

DECAF: Deep Extreme Classification with Label Features

https://github.com/GanjinZero/CODER

공식 코드

CODER: Knowledge infused cross-lingual medical term embedding for term normalization

https://github.com/GhadaSokar/AFAF

공식 코드

Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks

https://github.com/Gompyn/re2com-opensource

공식 코드

Retrieve and Refine: Exemplar-based Neural Comment Generation

https://github.com/HELL-TO-HEAVEN/KGE-CL

공식 코드

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

https://github.com/Hang422/causal-thought-graph

공식 코드

Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs

https://github.com/HideUnderBush/UI2I_via_StyleGAN2

공식 코드

Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network

https://github.com/HobbitLong/SupContrast

공식 코드

Rethinking the Sample Relations for Few-Shot Classification

https://github.com/IBM/Retrieval-Enhanced-Transformer-Little

공식 코드

Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift Generalization

https://github.com/KB-Ding/Multi-stage-Distillaton-Framework

공식 코드

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching

https://github.com/LIN-SHANG/InstructERC

공식 코드

InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models

https://github.com/LinQinLiang/SSAH-adversarial-attack

공식 코드

Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity

https://github.com/LittleGuoKe/ConceptFERE

공식 코드

Entity Concept-enhanced Few-shot Relation Extraction

https://github.com/Lolo1222/EquivPruner

공식 코드

EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning

https://github.com/MI2DataLab/nlp_interpretability_framework

공식 코드

Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.