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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 3301–3325 of 4002 papers

TitleStatusHype
End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-levelCode0
Ontology-Aware Token Embeddings for Prepositional Phrase AttachmentCode0
Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification—0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
Wordnet extension via word embeddings: Experiments on the Norwegian Wordnet—0
Automatic Morpheme Segmentation and Labeling in Universal Dependencies ResourcesCode0
Word vectors, reuse, and replicability: Towards a community repository of large-text resources—0
Finnish resources for evaluating language model semanticsCode0
The Making of the Royal Society Corpus—0
Model Transfer for Tagging Low-resource Languages using a Bilingual DictionaryCode0
Extending and Improving Wordnet via Unsupervised Word Embeddings—0
Neural Word Segmentation with Rich PretrainingCode0
Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts—0
Streaming Word Embeddings with the Space-Saving AlgorithmCode0
Watset: Automatic Induction of Synsets from a Graph of SynonymsCode0
A Trie-Structured Bayesian Model for Unsupervised Morphological Segmentation—0
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMsCode0
Cross-domain Semantic Parsing via ParaphrasingCode0
Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain—0
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification—0
Baselines and test data for cross-lingual inferenceCode0
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word EmbeddingsCode0
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?—0
Incremental Skip-gram Model with Negative SamplingCode0
Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization—0
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