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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 1651–1675 of 4002 papers

TitleStatusHype
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains—0
Generic and Specialized Word Embeddings for Multi-Domain Machine Translation—0
Generic Embedding-Based Lexicons for Transparent and Reproducible Text Scoring—0
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction—0
Geographical Evaluation of Word Embeddings—0
Geographically-Balanced Gigaword Corpora for 50 Language Varieties—0
Compressing Word Embeddings—0
Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings—0
Compressing Word Embeddings Using Syllables—0
Getting the \#\#life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?—0
GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics—0
A Structured Distributional Model of Sentence Meaning and Processing—0
Detecting and Mitigating Indirect Stereotypes in Word Embeddings—0
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts—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
GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach.—0
GlobalTrait: Personality Alignment of Multilingual Word Embeddings—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues—0
Gloss Alignment Using Word Embeddings—0
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings—0
GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering—0
An evaluation of Czech word embeddings—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French—0
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