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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 3026–3050 of 4002 papers

TitleStatusHype
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a DiscourseCode0
Using k-way Co-occurrences for Learning Word Embeddings—0
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—0
Learning Neural Word Salience ScoresCode0
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects—0
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging—0
Cross-Lingual Word Representations: Induction and Evaluation—0
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings—0
Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees—0
Translating Dialectal Arabic as Low Resource Language using Word Embedding—0
Using Gaze Data to Predict Multiword Expressions—0
Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach—0
Finding Individual Word Sense Changes and their Delay in Appearance—0
We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!Code0
MappSent: a Textual Mapping Approach for Question-to-Question Similarity—0
Detecting Metaphorical Phrases in the Polish Language—0
Gender Prediction for Chinese Social Media Data—0
Word Embeddings for Multi-label Document Classification—0
Word Embeddings as Features for Supervised Coreference Resolution—0
Extracting Tags from Large Raw Texts Using End-to-End Memory Networks—0
Fully Delexicalized Contexts for Syntax-Based Word Embeddings—0
What do we need to know about an unknown word when parsing German—0
Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games—0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction—0
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