https://github.com/jean-kunz/ml_research_papers
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
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/jen1995/fastText
https://github.com/jiajunhua/facebookresearch-MUSE
https://github.com/jiesutd/NCRFpp
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/jiesutd/PyTorchSeqLabel
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/jiesutd/yato
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/jobdataexchange/competensor
https://github.com/johnr0/TaleBrush-backend
https://github.com/johntailor/ft-topic
Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences
https://github.com/joonasrooben/NLP-text2emoji
emoji2vec: Learning Emoji Representations from their Description
https://github.com/joseph-bongo-220/TV_NLP_Project
https://github.com/julianser/hed-dlg-truncated
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
https://github.com/junronglau/CS421_Project
Siamese CBOW: Optimizing Word Embeddings for Sentence Representations
https://github.com/jvdbogae/artverc
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/kaliahinartem/twitter_sentiment_analysis
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
https://github.com/kamalkraj/Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs
https://github.com/kamperh/bucktsong_eskmeans
An embedded segmental K-means model for unsupervised segmentation and clustering of speech
https://github.com/kamperh/eskmeans
An embedded segmental K-means model for unsupervised segmentation and clustering of speech
https://github.com/kamperh/recipe_bucktsong_awe
https://github.com/kamperh/recipe_bucktsong_awe_py3
https://github.com/kamperh/recipe_semantic_flickraudio
Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings
https://github.com/karanthakkar97/Text-summarization-with-Seq2Seq
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
https://github.com/kevinlu1248/pyate
https://github.com/kevinwong2013/COMS4995_Team_4_Zero_Shot_Classifier
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
https://github.com/konstapo/2022-fake-news-mediaeval-task
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
https://github.com/korymath/jann
https://github.com/kpu/fastertext
https://github.com/krisbukovi/document_similarity
https://github.com/kudkudak/word-embeddings-benchmarks
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks
https://github.com/kunde122/bilm-tf
Evaluation of sentence embeddings in downstream and linguistic probing tasks
https://github.com/kurtisdavid/MetaOrthogonalization
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
https://github.com/kushalchauhan98/dynamic-meta-embeddings
Dynamic Meta-Embeddings for Improved Sentence Representations
https://github.com/labdac/charlacompling
https://github.com/labmlai/annotated_deep_learning_paper_implementations
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/lamantinushka/Time-dependent-embeddings
https://github.com/leelaylay/TweetSemEval
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.