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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 851–875 of 4002 papers

TitleStatusHype
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
Context-Aware Neural Machine Translation Decoding—0
Context-Dependent Sense Embedding—0
A comparative analysis of embedding models for patent similarity—0
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification—0
A Survey On Neural Word Embeddings—0
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content—0
Context-Sensitive Malicious Spelling Error Correction—0
Context Sensitive Neural Lemmatization with Lematus—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
Contextual Aware Joint Probability Model Towards Question Answering System—0
Contextual Document Embeddings—0
Contextual Embeddings: When Are They Worth It?—0
A novel methodology on distributed representations of proteins using their interacting ligands—0
Contextualized context2vec—0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Cross-lingual hate speech detection based on multilingual domain-specific word embeddings—0
A Novel Method of Extracting Topological Features from Word Embeddings—0
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