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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 1571–1580 of 4002 papers

TitleStatusHype
Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings—0
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment—0
Distributional Semantics for Neo-Latin—0
A Gradient Boosting-Seq2Seq System for Latin POS Tagging and Lemmatization—0
Morphological Disambiguation of South S\'ami with FSTs and Neural Networks—0
Graph Exploration and Cross-lingual Word Embeddings for Translation Inference Across Dictionaries—0
LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries—0
Analyzing the Surprising Variability in Word Embedding Stability Across LanguagesCode0
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