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/beyondguo/STA
공식 코드Selective Text Augmentation with Word Roles for Low-Resource Text Classification
https://github.com/bflashcp3f/deer
공식 코드Label-Guided In-Context Learning for Named Entity Recognition
https://github.com/bids-xu-lab/biomedical-nlp-benchmarks
공식 코드https://github.com/bigvideoresearch/Enigma
공식 코드https://github.com/binwang28/evalrank-embedding-evaluation
공식 코드Just Rank: Rethinking Evaluation with Word and Sentence Similarities
https://github.com/bio-ontology-research-group/opa2vec
공식 코드https://github.com/blurryface0814/pcpnet
공식 코드PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction
https://github.com/bohanli/BERT-flow
공식 코드https://github.com/boracaglayan/bis-nl2sql
공식 코드BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios
https://github.com/brandeis-llc/L-CSTS
공식 코드https://github.com/bravegroup/psps
공식 코드https://github.com/buraksatar/RoME_video_retrieval
공식 코드https://github.com/cambridgeltl/mirror-bert
공식 코드https://github.com/camma-public/surglatentgraph
공식 코드Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning
https://github.com/casperhansen/PairRec
공식 코드https://github.com/catrin-baze/ino-of-factify
공식 코드INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification
https://github.com/cbluebenchmark/cblue
공식 코드CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
https://github.com/cedricdeboom/RepresentationLearning
공식 코드Representation learning for very short texts using weighted word embedding aggregation
https://github.com/cgnorthcutt/forum-diversification
공식 코드https://github.com/cgshpi/topic-models-and-dimensionality-reduction-benchmark
공식 코드https://github.com/chaojiang06/wiki-auto
공식 코드Neural CRF Model for Sentence Alignment in Text Simplification
https://github.com/chendelong1999/linguistic-similarity
공식 코드Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models
https://github.com/chuzhumin98/convsearch-dataset
공식 코드Towards Better Understanding of User Satisfaction in Open-Domain Conversational Search
https://github.com/clovaai/symmetrical-synthesis
공식 코드https://github.com/colinlu50/hydratext
공식 코드Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization
https://github.com/cpjku/performance_embeddings_fire23
공식 코드https://github.com/cuiniki/llms-are-not-scorers
공식 코드LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
https://github.com/cvjena/semantic-embeddings
공식 코드Hierarchy-based Image Embeddings for Semantic Image Retrieval
https://github.com/cycraft-corp/CmdCaliper
공식 코드CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research
https://github.com/cyr19/menli
공식 코드MENLI: Robust Evaluation Metrics from Natural Language Inference
https://github.com/daiyongya/markbert
공식 코드https://github.com/danieljunior/jidm
공식 코드https://github.com/ddangelov/Top2Vec
공식 코드https://github.com/deepsoftwareanalytics/race
공식 코드https://github.com/dell-research-harvard/linktransformer
공식 코드LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.