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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/lfmatosm/embedded-topic-model
https://github.com/lilytang2017/semeval2020
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
https://github.com/linkfluence/fastText4j
https://github.com/liuhaixiachina/Sentiment-Analysis-of-Citations-Using-Word2vec
https://github.com/lmd1993/fastTextBoost
https://github.com/longdt219/xlingualemb
Learning Crosslingual Word Embeddings without Bilingual Corpora
https://github.com/lopezbec/COVID19_Tweets_Dataset
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
https://github.com/lopezbec/COVID19_Tweets_Dataset_2020
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
https://github.com/lucidrains/PaLM-jax
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/luckyPT/jvm-ml
https://github.com/luhuiguo/jfasttext
https://github.com/madcpt/pretrained-embeddings-toolkit
From Paraphrase Database to Compositional Paraphrase Model and Back
https://github.com/maexe/linspector-web
LINSPECTOR: Multilingual Probing Tasks for Word Representations
https://github.com/maochf/MUSE
https://github.com/marekrei/sdsn
Scoring Lexical Entailment with a Supervised Directional Similarity Network
https://github.com/marfoq/unsupervised_translator
https://github.com/mario-holubar/ATCS-InferSent
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/martinambition/IntentClassificationBenchmark
Subword Semantic Hashing for Intent Classification on Small Datasets
https://github.com/martinomensio/spacy-universal-sentence-encoder
https://github.com/mdiannna/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs-adaptation
https://github.com/megvii-research/ml-gcn
Multi-Label Image Recognition with Graph Convolutional Networks
https://github.com/menajosep/AleatoricSent
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/mgulcin/DL_Rec
https://github.com/michaelfarrell76/End-To-End-Generative-Dialogue
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
https://github.com/microsoft/recommenders
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
https://github.com/migrationsKB/MGKB
https://github.com/mingdachen/bilm-tf
Evaluation of sentence embeddings in downstream and linguistic probing tasks
https://github.com/mingu600/compositional_code_learning
Compressing Word Embeddings via Deep Compositional Code Learning
https://github.com/minhpqn/vietner
Attentive Neural Network for Named Entity Recognition in Vietnamese
https://github.com/mleimeister/document-image-classification
Multimodal deep networks for text and image-based document classification
https://github.com/monishap123/mlhc2020
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
https://github.com/mrzzy/np-dl-assign-2
https://github.com/mshehrozsajjad/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs
https://github.com/mtbarta/hyperbolic
https://github.com/mtrusca/HAABSA_PLUS_PLUS
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.