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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 2801–2825 of 4002 papers

TitleStatusHype
Literal or idiomatic? Identifying the reading of single occurrences of German multiword expressions using word embeddings—0
LIT Team's System Description for Japanese-Chinese Machine Translation Task in IWSLT 2020—0
LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020—0
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space—0
Local-Global Vectors to Improve Unigram Terminology Extraction—0
Local Homology of Word Embeddings—0
Locality Preserving Sentence Encoding—0
Locally-Contextual Nonlinear CRFs for Sequence Labeling—0
Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction—0
Looking for a Role for Word Embeddings in Eye-Tracking Features Prediction: Does Semantic Similarity Help?—0
Looking Into the Black Box - How Are Idioms Processed in BERT?—0
L'optimisation du plongement de mots pour le fran : une application de la classification des phrases (Optimization of Word Embeddings for French : an Application of Sentence Classification)—0
Loss Decomposition for Fast Learning in Large Output Spaces—0
Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings—0
Low-resource bilingual lexicon extraction using graph based word embeddings—0
Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages—0
LSTM CCG Parsing—0
LSTM Easy-first Dependency Parsing with Pre-trained Word Embeddings and Character-level Word Embeddings in Vietnamese—0
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories—0
LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance—0
LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines—0
LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text—0
LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and Vector Representations—0
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