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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 2601–2650 of 4002 papers

TitleStatusHype
Transparent, Efficient, and Robust Word Embedding Access with WOMBATCode0
Loss Decomposition for Fast Learning in Large Output Spaces—0
Improving Optimization in Models With Continuous Symmetry Breaking—0
Inter and Intra Topic Structure Learning with Word EmbeddingsCode0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
Predicting Brain Activation with WordNet EmbeddingsCode0
Predicting Concreteness and Imageability of Words Within and Across Languages via Word EmbeddingsCode0
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity—0
A Hybrid Learning Scheme for Chinese Word Embedding—0
Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in Algerian texts—0
Code-Switched Named Entity Recognition with Embedding Attention—0
Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimizationCode0
Comparison of Representations of Named Entities for Document Classification—0
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline—0
Word Embeddings-Based Uncertainty Detection in Financial Disclosures—0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing—0
Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks—0
Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts—0
Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus—0
Natural Language Inference with Definition Embedding Considering Context On the Fly—0
Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification—0
Attention-based Semantic Priming for Slot-filling—0
Joint learning of frequency and word embeddings for multilingual readability assessment—0
A Sequence Learning Method for Domain-Specific Entity Linking—0
WordNet EmbeddingsCode0
A Helping Hand: Transfer Learning for Deep Sentiment Analysis—0
Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings—0
Orthographic Features for Bilingual Lexicon Induction—0
Named Entity Recognition With Parallel Recurrent Neural NetworksCode0
Unsupervised Learning of Distributional Relation Vectors—0
Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations—0
A Multi-task Approach to Learning Multilingual Representations—0
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
A Deep Relevance Model for Zero-Shot Document FilteringCode0
Using pseudo-senses for improving the extraction of synonyms from word embeddings—0
Word Embedding and WordNet Based Metaphor Identification and Interpretation—0
Incorporating Latent Meanings of Morphological Compositions to Enhance Word EmbeddingsCode0
Addressing Noise in Multidialectal Word Embeddings—0
Batch IS NOT Heavy: Learning Word Representations From All Samples—0
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly ApplicableCode0
Multi-lingual Entity Discovery and Linking—0
Towards Understanding the Geometry of Knowledge Graph EmbeddingsCode0
Neural Sparse Topical Coding—0
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