Semantic Similarity
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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://github.com/dipamgoswami/fscil-calibration
공식 코드https://github.com/dipta007/semeval24-task8
공식 코드https://github.com/divettemarco/pragsim
공식 코드A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances
https://github.com/dll-wu/is-cse
공식 코드Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding
https://github.com/doribear/A-Novel-Patent-Similarity-Measurement-Methodology
공식 코드A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance
https://github.com/dsfsi/embedding-eval-data
공식 코드https://github.com/dukesun99/ACL-PRISM
공식 코드https://github.com/dvlab-research/rival
공식 코드Real-World Image Variation by Aligning Diffusion Inversion Chain
https://github.com/e-qin/gear
공식 코드https://github.com/echopluto/fhda
공식 코드https://github.com/elakhatibi/cdf-rag
공식 코드CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation
https://github.com/eleutherai/sae-auto-interp
공식 코드Automatically Interpreting Millions of Features in Large Language Models
https://github.com/ernlavr/multihal
공식 코드MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
https://github.com/erxinyu/disbert
공식 코드Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
https://github.com/eumesy/wrd
공식 코드https://github.com/faceless0124/mghstn
공식 코드Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity
https://github.com/fduxuan/sdm-attack
공식 코드Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making
https://github.com/flipz357/smatchpp
공식 코드SMATCH++: Standardized and Extended Evaluation of Semantic Graphs
https://github.com/forrestbao/suenes
공식 코드https://github.com/francescocappio/resend
공식 코드https://github.com/fraunhoferivi/ssp
공식 코드https://github.com/ftvalentini/embeddingsbiasfrequency
공식 코드The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings
https://github.com/ftvalentini/embeddingsfrequency
공식 코드Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics
https://github.com/galgreshler/Catch-A-Waveform
공식 코드Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
https://github.com/galprz/simqa
공식 코드https://github.com/gaopengcuhk/ballad
공식 코드A Simple Long-Tailed Recognition Baseline via Vision-Language Model
https://github.com/gbouritsas/cvpr18_multimodal_weakly_supervised_learning
공식 코드Multimodal Visual Concept Learning with Weakly Supervised Techniques
https://github.com/georgestein/galaxy_search
공식 코드Self-supervised similarity search for large scientific datasets
https://github.com/gingasan/adversarialsa
공식 코드https://github.com/gingl/earn
공식 코드Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
https://github.com/google-research/google-research
공식 코드Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval
https://github.com/gsk-ai/rambla
공식 코드RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
https://github.com/gucioopielka/saliency-semantic-rsa
공식 코드Saliency Suppressed, Semantics Surfaced: Visual Transformations in Neural Networks and the Brain
https://github.com/guxiao0822/lt-ds
공식 코드Tackling Long-Tailed Category Distribution Under Domain Shifts
https://github.com/gwohlgen/thai_word_similarity
공식 코드Word Similarity Datasets for Thai: Construction and Evaluation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.