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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/bharathrajcl/A-Robust-Hybrid-Approach-for-Textual-Document-Classification
A Robust Hybrid Approach for Textual Document Classification
https://github.com/bharathrajcl/Multimodal-deep-networks-for-text-and-image-based-document-classification
Multimodal deep networks for text and image-based document classification
https://github.com/bhoov/flyvec
https://github.com/blcunlp/CNLI
Analogical Reasoning on Chinese Morphological and Semantic Relations
https://github.com/bnosac/ETM
https://github.com/boknilev/nmt-repr-analysis
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/bollegala/repseval
https://github.com/bombdiggity/paper-bag
https://github.com/brkkaya/a_linear_attention_with_biases
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/bung87/fastText
https://github.com/caroline171/content_based_recommendation
https://github.com/cbaziotis/ntua-slp-semeval2018
NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention
https://github.com/cdpierse/transformers-interpret
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/celento/sent2vec
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
https://github.com/ceshine/textrank_summary_benchmark
https://github.com/chalothon/Sentence2Vec
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
https://github.com/changji2069/Scope-Project
https://github.com/changji2069/literature-review
https://github.com/cheng18/bilm-tf
Evaluation of sentence embeddings in downstream and linguistic probing tasks
https://github.com/christiaanjacobs/apply_awe
https://github.com/christiaanjacobs/globalphone_awe_pytorch
https://github.com/chschock/textsplit
https://github.com/cisnlp/simalign
https://github.com/cjmay/word2vec
https://github.com/claws-lab/dygetviz
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
https://github.com/code2k13/nlphose
https://github.com/code2k13/nlppipe
https://github.com/contemn1/sentence_embeddings
https://github.com/cran/topicmodels.etm
https://github.com/csebuetnlp/banglabert
AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages
https://github.com/cstorm125/thai2fit
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/cui0523/Code6/tree/main/fasttext
https://github.com/currentsapi/fastlangid
https://github.com/dadebias/genda-lens
https://github.com/dantetam/twitterServerAuth
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.