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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 3851–3875 of 4002 papers

TitleStatusHype
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces—0
Gaussian LDA for Topic Models with Word EmbeddingsCode0
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification—0
Detecting Semantically Equivalent Questions in Online User Forums—0
Enhancing the Inside-Outside Recursive Neural Network Reranker for Dependency Parsing—0
Semantic Clustering and Convolutional Neural Network for Short Text Categorization—0
Hallym: Named Entity Recognition on Twitter with Word Representation—0
Lexical Comparison Between Wikipedia and Twitter Corpora by Using Word Embeddings—0
An analysis of the user occupational class through Twitter content—0
The DCU Discourse Parser for Connective, Argument Identification and Explicit Sense Classification—0
A Multitask Objective to Inject Lexical Contrast into Distributional Semantics—0
A Minimalist Approach to Shallow Discourse Parsing and Implicit Relation Recognition—0
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies—0
A Lexicalized Tree Kernel for Open Information Extraction—0
Word Embeddings Pointing the Way for Late AntiquityCode0
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity—0
A Hierarchical Knowledge Representation for Expert Finding on Social Media—0
Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction—0
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction—0
Machine Comprehension with Syntax, Frames, and Semantics—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks—0
Distilling Word Embeddings: An Encoding Approach—0
Leveraging Word Embeddings for Spoken Document Summarization—0
Learning language through picturesCode0
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