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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 3801–3825 of 4002 papers

TitleStatusHype
Specializing Word Embeddings for Similarity or Relatedness—0
Sentence Compression by Deletion with LSTMs—0
Pre-Computable Multi-Layer Neural Network Language Models—0
Using reading behavior to predict grammatical functions—0
Exploiting Debate Portals for Semi-Supervised Argumentation Mining in User-Generated Web DiscourseCode0
Semi-supervised Dependency Parsing using Bilexical Contextual Features from Auto-Parsed Data—0
Exploring Word Embedding for Drug Name Recognition—0
Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining—0
Towards a Model of Prediction-based Syntactic Category Acquisition: First Steps with Word Embeddings—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words—0
Any-language frame-semantic parsing—0
What's in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Empty Category Detection using Path Features and Distributed Case Frames—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Improving evaluation and optimization of MT systems against MEANT—0
Component-Enhanced Chinese Character Embeddings—0
Better Summarization Evaluation with Word Embeddings for ROUGECode0
Learning Meta-Embeddings by Using Ensembles of Embedding SetsCode0
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning—0
Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels—0
Document Embedding with Paragraph VectorsCode0
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds—0
Clustering is Efficient for Approximate Maximum Inner Product Search—0
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