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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 1851–1900 of 4002 papers

TitleStatusHype
Deep Text Mining of Instagram Data Without Strong SupervisionCode0
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summariesCode0
Generating Timelines by Modeling Semantic ChangeCode0
Low-Rank Approximation of Matrices for PMI-based Word Embeddings—0
Cross-lingual Dependency Parsing with Unlabeled Auxiliary LanguagesCode0
Triplet-Aware Scene Graph Embeddings—0
CogniVal: A Framework for Cognitive Word Embedding EvaluationCode0
Multi-sense Definition Modeling using Word Sense Decompositions—0
Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind—0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Hierarchical Meta-Embeddings for Code-Switching Named Entity RecognitionCode0
Do We Need Neural Models to Explain Human Judgments of Acceptability?—0
Decision-Directed Data DecompositionCode0
Multi Sense Embeddings from Topic Models—0
Cross-Lingual BERT Transformation for Zero-Shot Dependency ParsingCode0
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector SpacesCode0
Retrofitting Contextualized Word Embeddings with Paraphrases—0
A Robust Hybrid Approach for Textual Document ClassificationCode0
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary InductionCode0
Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classificationCode0
Query Obfuscation Semantic Decomposition—0
Hope Speech Detection: A Computational Analysis of the Voice of Peace—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Multimodal Embeddings from Language ModelsCode0
Definition Frames: Using Definitions for Hybrid Concept RepresentationsCode0
Follow the Leader: Documents on the Leading Edge of Semantic Change Get More CitationsCode0
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?—0
Composing Knowledge Graph Embeddings via Word Embeddings—0
Distributed Training of Embeddings using Graph Analytics—0
Specializing Unsupervised Pretraining Models for Word-Level Semantic SimilarityCode0
Semantics-aware BERT for Language UnderstandingCode0
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank—0
Fusing Vector Space Models for Domain-Specific Applications—0
Examining Gender Bias in Languages with Grammatical GenderCode0
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Empirical Study of Diachronic Word Embeddings for Scarce Data—0
Affect Enriched Word Embeddings for News Information Retrieval—0
Interpretable Word Embeddings via Informative Priors—0
Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRFCode0
On the Downstream Performance of Compressed Word EmbeddingsCode0
How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 EmbeddingsCode0
It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution—0
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space—0
Toward Multilingual Identification of Online Registers—0
Political Stance in DanishCode0
Unsupervised Inference of Object Affordance from Text Corpora—0
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language—0
Named-Entity Recognition for NorwegianCode0
Towards High Accuracy Named Entity Recognition for Icelandic—0
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