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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 1551–1575 of 4002 papers

TitleStatusHype
Fast query-by-example speech search using separable model—0
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings—0
Detecting Cybersecurity Events from Noisy Short Text—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
Feasibility of BERT Embeddings For Domain-Specific Knowledge Mining—0
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition—0
Feature Embedding for Dependency Parsing—0
Feature Engineering vs BERT on Twitter Data—0
A New Approach to Animacy Detection—0
FeelsGoodMan: Inferring Semantics of Twitch Neologisms—0
Self-supervised audio representation learning for mobile devices—0
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language—0
A Simple Fully Connected Network for Composing Word Embeddings from Characters—0
A mostly unlexicalized model for recognizing textual entailment—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
Combining word embeddings and convolutional neural networks to detect duplicated questions—0
Field Embedding: A Unified Grain-Based Framework for Word Representation—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Figure Me Out: A Gold Standard Dataset for Metaphor Interpretation—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
Finding Individual Word Sense Changes and their Delay in Appearance—0
Finding People's Professions and Nationalities Using Distant Supervision - The FMI@SU "goosefoot" team at the WSDM Cup 2017 Triple Scoring Task—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings—0
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