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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/bakarov/2ch2vec

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Automated Detection of Non-Relevant Posts on the Russian Imageboard "2ch": Importance of the Choice of Word Representations

https://github.com/bakarov/cross-lang-embeddings

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The Limitations of Cross-language Word Embeddings Evaluation

https://github.com/barahona-research-group/LGDE

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LGDE: Local Graph-based Dictionary Expansion

https://github.com/bcbi-edu/p_eickhoff_isoscore

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IsoScore: Measuring the Uniformity of Embedding Space Utilization

https://github.com/bcmi/CaGNet-Zero-Shot-Semantic-Segmentation

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Context-aware Feature Generation for Zero-shot Semantic Segmentation

https://github.com/bdavis56/DDDD

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Decision-Directed Data Decomposition

https://github.com/bdqnghi/hierarchical-programming-language-mapping

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Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code

https://github.com/beamandrew/cui2vec

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Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

https://github.com/belph/wiki-sem-500

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Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations

https://github.com/benathi/density-order-emb

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Hierarchical Density Order Embeddings

https://github.com/benathi/multisense-prob-fasttext

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Probabilistic FastText for Multi-Sense Word Embeddings

https://github.com/benathi/word2gm

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Multimodal Word Distributions

https://github.com/bgshin/distill_demo

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The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning

https://github.com/bhavyaghai/WordBias

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WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings

https://github.com/bheinzerling/bpemb

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BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages

https://github.com/bigganbing/Fairseq_MorphTE

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MorphTE: Injecting Morphology in Tensorized Embeddings

https://github.com/billy-inn/NFETC

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Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss

https://github.com/binbinjiang/context2vector

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Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings

https://github.com/bionlproc/BERT-CRel-Embeddings

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Improved Biomedical Word Embeddings in the Transformer Era

https://github.com/bladewaltz1/modecap

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Learning Distinct and Representative Styles for Image Captioning

https://github.com/blcuicall/semeval2022-task1-dm

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BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling

https://github.com/blei-lab/causal-text-embeddings

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Adapting Text Embeddings for Causal Inference

https://github.com/blester125/multi-look-embeddings

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Multiple Word Embeddings for Increased Diversity of Representation

https://github.com/blester125/word-vectors

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Leader: Prefixing a Length for Faster Word Vector Serialization

https://github.com/bloomberg/koan

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Corrected CBOW Performs as well as Skip-gram

https://github.com/bobxwu/fastopic

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FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model

https://github.com/bobxwu/topmost

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FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model

https://github.com/bootphon/measuring-regularities-in-word-embeddings

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Analogies minus analogy test: measuring regularities in word embeddings

https://github.com/bootphon/paraphrases_do_not_explain_analogies

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Paraphrases do not explain word analogies

https://github.com/boschresearch/adversarial_meta_embeddings

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FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations

https://github.com/boschresearch/steps-parser

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Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary

https://github.com/boun-tabi/chemboost

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ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction

https://github.com/bplank/ijcnlp2017-customer-feedback

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ALL-IN-1: Short Text Classification with One Model for All Languages

https://github.com/brcsomnath/kram

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Robust Concept Erasure via Kernelized Rate-Distortion Maximization

https://github.com/bright2013/CorefAwareMRC

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Tracing Origins: Coreference-aware Machine Reading Comprehension

https://github.com/brownfortress/llms-trc

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Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?

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