https://github.com/mattwright99/thesis
공식 코드Design and Implementation of a Quantum Kernel for Natural Language Processing
Papers with Code 아카이브
Word Embeddings
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Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.
Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.
<span style="color:grey; opacity: 0.6">( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/maximecb/gym-miniworld
공식 코드Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
https://github.com/mbiesialska/wgan-postspec
공식 코드Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources
https://github.com/mchen24/iclr2017
공식 코드Efficient Vector Representation for Documents through Corruption
https://github.com/mebrunet/understanding-bias
공식 코드https://github.com/melvinwevers/historical_concepts
공식 코드Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990
https://github.com/mengf1/trpos
공식 코드Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary
https://github.com/mgormley/pacaya
공식 코드Improved Relation Extraction with Feature-Rich Compositional Embedding Models
https://github.com/mgruppi/schme
공식 코드SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
https://github.com/mikahama/natas
공식 코드https://github.com/mingdachen/vsl
공식 코드Variational Sequential Labelers for Semi-Supervised Learning
https://github.com/minzwon/tag-based-music-retrieval
공식 코드https://github.com/mirunabetianu/dallmi
공식 코드DALLMi: Domain Adaption for LLM-based Multi-label Classifier
https://github.com/mitmedialab/HCU400
공식 코드HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty
https://github.com/mitmedialab/sherlock-project
공식 코드Sherlock: A Deep Learning Approach to Semantic Data Type Detection
https://github.com/mlepori1/Unequal_Representations
공식 코드https://github.com/mohammedmohsinali/pesto
공식 코드PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages
https://github.com/mojtaba-Hsafa/train_accidents
공식 코드Analysis of Railway Accidents' Narratives Using Deep Learning
https://github.com/monisha-jega/paraphrasing_embedding_outputs
공식 코드Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs
https://github.com/moore3930/beyondsharedvocabulary
공식 코드https://github.com/mtoshevska/Comparing-Embeddings
공식 코드Comparative Analysis of Word Embeddings for Capturing Word Similarities
https://github.com/muhaochen/bilingual_dictionaries
공식 코드https://github.com/mxeddie/demagnosticdebias
공식 코드A Robust Bias Mitigation Procedure Based on the Stereotype Content Model
https://github.com/mxhofer/Named-Entity-Recognition-BidirectionalLSTM-CNN-CoNLL
공식 코드Few-shot Learning for Named Entity Recognition in Medical Text
https://github.com/my-yy/s2v_rc
공식 코드https://github.com/mysong7nlper/hypermatch
공식 코드Hyperbolic Relevance Matching for Neural Keyphrase Extraction
https://github.com/mzichert/scd_vp
공식 코드Tracing the Development of the Virtual Particle Concept Using Semantic Change Detection
https://github.com/nafisa67/thesis
공식 코드https://github.com/naist-nlp/tubelex
공식 코드Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?
https://github.com/namir0806/flag
공식 코드FLAG: Financial Long Document Classification via AMR-based GNN
https://github.com/narutatsuri/cwm
공식 코드Contrastive Loss is All You Need to Recover Analogies as Parallel Lines
https://github.com/naserahmadi/tdmatch
공식 코드https://github.com/navid-aub/linkedin-dataset
공식 코드https://github.com/ncbi-nlp/BioSentVec
공식 코드BioSentVec: creating sentence embeddings for biomedical texts
https://github.com/ncbi-nlp/BioWordVec
공식 코드BioSentVec: creating sentence embeddings for biomedical texts
https://github.com/neulab/awesome-align
공식 코드Word Alignment by Fine-tuning Embeddings on Parallel Corpora
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.