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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

Showing 38263850 of 4002 papers

TitleStatusHype
Evaluating Metrics for Bias in Word Embeddings0
Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms0
Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation0
Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks0
Evaluating Neural Word Representations in Tensor-Based Compositional Settings0
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning0
Evaluating Sub-word Embeddings in Cross-lingual Models0
Evaluating the Consistency of Word Embeddings from Small Data0
Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi'kmaq Language Modelling0
Evaluating the Stability of Embedding-based Word Similarities0
Evaluating the timing and magnitude of semantic change in diachronic word embedding models0
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings0
Evaluating vector-space models of analogy0
Evaluating Word Embedding Hyper-Parameters for Similarity and Analogy Tasks0
Evaluating Word Embedding Models: Methods and Experimental Results0
Evaluating Word Embeddings for Indonesian--English Code-Mixed Text Based on Synthetic Data0
Evaluating Word Embeddings for Language Acquisition0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts0
Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri0
Evaluating Word Embeddings on Low-Resource Languages0
Evaluating Word Embeddings Using a Representative Suite of Practical Tasks0
Evaluating word embeddings with fMRI and eye-tracking0
Evaluation Framework for Understanding Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms0
Evaluation methods for unsupervised word embeddings0
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