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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 1701–1725 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
Hyperspherical Query Likelihood Models with Word Embeddings—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
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity—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
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification—0
Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings—0
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