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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 3526–3550 of 4002 papers

TitleStatusHype
Language Models with Pre-Trained (GloVe) Word EmbeddingsCode0
A Dynamic Window Neural Network for CCG Supertagging—0
Neural-based Noise Filtering from Word EmbeddingsCode0
Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database—0
Neural Structural Correspondence Learning for Domain AdaptationCode0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
Chinese Event Extraction Using DeepNeural Network with Word Embedding—0
Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks—0
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings—0
基於相依詞向量的剖析結果重估與排序(N-best Parse Rescoring Based on Dependency-Based Word Embeddings)—0
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks—0
emoji2vec: Learning Emoji Representations from their DescriptionCode0
Topic Modeling over Short Texts by Incorporating Word EmbeddingsCode0
Creating Causal Embeddings for Question Answering with Minimal Supervision—0
Ask the GRU: Multi-Task Learning for Deep Text Recommendations—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
Hash2Vec, Feature Hashing for Word Embeddings—0
Testing APSyn against Vector Cosine on Similarity Estimation—0
Learning Word Embeddings from Intrinsic and Extrinsic Views—0
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments—0
Redefining part-of-speech classes with distributional semantic models—0
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation—0
UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval—0
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