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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 1601–1625 of 4002 papers

TitleStatusHype
Frequency-based Distortions in Contextualized Word Embeddings—0
Extracting Temporal and Causal Relations between Events—0
Extracting Tags from Large Raw Texts Using End-to-End Memory Networks—0
From communities to interpretable network and word embedding: an unified approach—0
Extracting Social Networks from Literary Text with Word Embedding Tools—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?—0
From meaning to perception -- exploring the space between word and odor perception embeddings—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
From Raw Text to Universal Dependencies - Look, No Tags!—0
Extracting Possessions from Social Media: Images Complement Language—0
Extracting domain-specific terms using contextual word embeddings—0
Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning—0
Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)—0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
Co-learning of Word Representations and Morpheme Representations—0
A semi-supervised model for Persian rumor verification based on content information—0
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications—0
Extending and Improving Wordnet via Unsupervised Word Embeddings—0
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts—0
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter—0
COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings—0
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