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Word Embeddings

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.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 1701–1750 of 4002 papers

TitleStatusHype
GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
Habibi - a multi Dialect multi National Arabic Song Lyrics Corpus—0
Hallym: Named Entity Recognition on Twitter with Word Representation—0
Handling Homographs in Neural Machine Translation—0
Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text—0
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
Hash2Vec, Feature Hashing for Word Embeddings—0
Hate and Offensive Speech Detection in Hindi and Marathi—0
Hate speech detection using static BERT embeddings—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French—0
BERT's Conceptual Cartography: Mapping the Landscapes of Meaning—0
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph—0
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets—0
Context-Aware Neural Machine Translation Decoding—0
Affordance Extraction and Inference based on Semantic Role Labeling—0
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment—0
HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence—0
Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models—0
Detecting and Mitigating Indirect Stereotypes in Word Embeddings—0
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification—0
A Survey On Neural Word Embeddings—0
Des repr\'esentations continues de mots pour l'analyse d'opinions en arabe: une \'etude qualitative (Word embeddings for Arabic sentiment analysis : a qualitative study)—0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings—0
HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding—0
An evaluation of Czech word embeddings—0
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes—0
Hostility Detection and Covid-19 Fake News Detection in Social Media—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
A Survey on Word Meta-Embedding Learning—0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings—0
How Cute is Pikachu? Gathering and Ranking Pokémon Properties from Data with Pokémon Word Embeddings—0
How does a Multilingual LM Handle Multiple Languages?—0
Contextual Document Embeddings—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
Contextual Embeddings: When Are They Worth It?—0
IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings—0
How much does a word weigh? Weighting word embeddings for word sense induction—0
How Much Does Tokenization Affect Neural Machine Translation?—0
How much do word embeddings encode about syntax?—0
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?—0
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent—0
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text—0
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network—0
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