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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 2601–2625 of 4002 papers

TitleStatusHype
Towards a unified framework for bilingual terminology extraction of single-word and multi-word termsCode0
Enhancing General Sentiment Lexicons for Domain-Specific Use—0
Authorless Topic Models: Biasing Models Away from Known StructureCode0
Enriching Word Embeddings with Domain Knowledge for Readability Assessment—0
Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings—0
Aggression Identification and Multi Lingual Word Embeddings—0
Gender Bias in Neural Natural Language ProcessingCode0
Clustering Prominent People and Organizations in Topic-Specific Text Corpora—0
Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large CorporaCode0
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder—0
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!Code0
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations—0
Imparting Interpretability to Word Embeddings while Preserving Semantic StructureCode0
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name TypingCode0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Tracking the Evolution of Words with Time-reflective Text Representations—0
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank ConcatenationCode0
Predicting Concreteness and Imageability of Words Within and Across Languages via Word EmbeddingsCode0
Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings—0
A Review of Different Word Embeddings for Sentiment Classification using Deep LearningCode0
Transparent, Efficient, and Robust Word Embedding Access with WOMBATCode0
Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations—0
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts—0
Incorporating Latent Meanings of Morphological Compositions to Enhance Word EmbeddingsCode0
Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus—0
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