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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 2676–2700 of 4002 papers

TitleStatusHype
Evaluation of sentence embeddings in downstream and linguistic probing tasksCode0
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable RepresentationsCode0
fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings—0
Learning Acoustic Word Embeddings with Temporal Context for Query-by-Example Speech Search—0
Diachronic word embeddings and semantic shifts: a survey—0
Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data—0
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient MobilityCode0
Finding Convincing Arguments Using Scalable Bayesian Preference LearningCode0
The Limitations of Cross-language Word Embeddings EvaluationCode0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
Closed Form Word Embedding Alignment—0
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction—0
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge—0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing—0
Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing—0
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings—0
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network—0
Specialising Word Vectors for Lexical EntailmentCode0
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
Stacking with Auxiliary Features for Visual Question Answering—0
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings—0
Subword-level Composition Functions for Learning Word Embeddings—0
Igbo Diacritic Restoration using Embedding Models—0
NEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network Model—0
NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model—0
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