https://github.com/yyaghoobzadeh/name_typing
공식 코드Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
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/yzhan238/seedtopicmine
공식 코드Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
https://github.com/zekavat-itrc/sentiment-analysis
공식 코드Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs
https://github.com/zenanz/ChemPatentEmbeddings
공식 코드Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
https://github.com/zeroix15/Twitter_Sarcasm_Detections
공식 코드Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word Embeddings
https://github.com/zh3nis/BPMI
공식 코드Squashed Shifted PMI Matrix: Bridging Word Embeddings and Hyperbolic Spaces
https://github.com/zh3nis/SGNS
공식 코드Squashed Shifted PMI Matrix: Bridging Word Embeddings and Hyperbolic Spaces
https://github.com/zhangmozhi/iternorm
공식 코드https://github.com/zhuchen03/FreeLB
공식 코드FreeLB: Enhanced Adversarial Training for Natural Language Understanding
https://github.com/zihangdai/mos
공식 코드Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
https://github.com/ziqizhang/data
공식 코드https://github.com/ziqizhang/semrerank
공식 코드https://github.com/ziyi-yang/GEM
공식 코드https://github.com/ziyin-dl/global-anchor-method
공식 코드The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
https://github.com/ziyin-dl/word-embedding-dimensionality-selection
공식 코드https://github.com/zizi1532/BasisCustomize
공식 코드Categorical Metadata Representation for Customized Text Classification
https://github.com/zjpbinary/CSCBLI
공식 코드Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction
https://github.com/zouharvi/pwesuite
공식 코드PWESuite: Phonetic Word Embeddings and Tasks They Facilitate
https://gitlab.com/chen00217/bert_subsumption
공식 코드Contextual Semantic Embeddings for Ontology Subsumption Prediction
https://gitlab.com/compstorylab/sentiment-analysis
공식 코드Augmenting semantic lexicons using word embeddings and transfer learning
https://gitlab.com/xiaohaima/lexical-dynamic-graph
공식 코드Graph-based Clustering for Detecting Semantic Change Across Time and Languages
https://gitlab.informatik.uni-bremen.de/covis1819/worth-the-attention
공식 코드https://github.com/1230113202/NV-JM-DD
https://github.com/123wowow123/chronopin
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
https://github.com/12kleingordon34/NLP_masters_project
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/fasttext
https://github.com/2023-MindSpore-4/Code12/tree/main/ganleilei/SemGloVeMindspore-master
https://github.com/2024-MindSpore-1/Code2/tree/main/tengzhiyang/LogiQA2.0-main
https://github.com/2024-MindSpore-1/Code2/tree/main/tengzhiyang/SemGloVeMindspore-master
https://github.com/2024-MindSpore-1/Code7/tree/main/CSNL
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
https://github.com/50kawa/mimick_chainer
https://github.com/AaruranE/Analogies
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
https://github.com/AaruranLog/Analogies
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
https://github.com/AdeDZY/K-NRM
https://github.com/AdiCovaci/urlnet_3class
URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
https://github.com/Ahren09/dygetviz
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.