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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 2301–2350 of 4002 papers

TitleStatusHype
Vector representations of text data in deep learning—0
Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents—0
Learning multilingual topics through aspect extraction from monolingual texts—0
Jabberwocky Parsing: Dependency Parsing with Lexical Noise—0
Inorganic Materials Synthesis Planning with Literature-Trained Neural NetworksCode0
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input—0
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings—0
What are the biases in my word embedding?—0
How Much Does Tokenization Affect Neural Machine Translation?—0
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence—0
The Global Anchor Method for Quantifying Linguistic Shifts and Domain AdaptationCode0
Detecting weak and strong Islamophobic hate speech on social media—0
Unsupervised domain-agnostic identification of product names in social media posts—0
Delta Embedding Learning—0
On the Dimensionality of Word EmbeddingCode0
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous OutputsCode0
Asynchronous Training of Word Embeddings for Large Text CorporaCode0
Are you tough enough? Framework for Robustness Validation of Machine Comprehension SystemsCode0
Building Sequential Inference Models for End-to-End Response SelectionCode0
Automatic classification of speech overlaps: Feature representation and algorithms—0
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions—0
Unsupervised Mining of Analogical Frames by Constraint SatisfactionCode0
A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions—0
Cluster Labeling by Word Embeddings and WordNet's Hypernymy—0
Modeling Speech Acts in Asynchronous Conversations: A Neural-CRF Approach—0
Incorporating Context into Language Encoding Models for fMRI—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems—0
Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach—0
Verb Argument Structure Alternations in Word and Sentence Embeddings—0
Combining neural and knowledge-based approaches to Named Entity Recognition in Polish—0
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data—0
Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings—0
Learning Relation Representations from Word Representations—0
NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction—0
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy—0
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors—0
HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal UncertaintyCode0
A Deterministic Algorithm for Bridging Anaphora Resolution—0
Few-shot Learning for Named Entity Recognition in Medical TextCode0
The Cinderella Complex: Word Embeddings Reveal ender Stereotypes in Movies and BooksCode0
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?—0
Learning Semantic Representations for Novel Words: Leveraging Both Form and ContextCode0
Neural sequence labeling for Vietnamese POS Tagging and NER—0
Deep Neural Networks for Query Expansion using Word Embeddings—0
microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF—0
Learning acoustic word embeddings with phonetically associated triplet network—0
Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification—0
Unsupervised Hyperalignment for Multilingual Word Embeddings—0
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction—0
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