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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 2451–2475 of 4002 papers

TitleStatusHype
Importance of Self-Attention for Sentiment Analysis—0
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Improved CCG Parsing with Semi-supervised Supertagging—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence—0
Improved Semantic Representation for Domain-Specific Entities—0
Improved Text Classification via Contrastive Adversarial Training—0
Improved Word Embeddings with Implicit Structure Information—0
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation—0
Improving Aspect-Level Sentiment Analysis with Aspect Extraction—0
Improving average ranking precision in user searches for biomedical research datasets—0
Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies—0
Improving Disfluency Detection by Self-Training a Self-Attentive Model—0
Improving Distributional Similarity with Lessons Learned from Word Embeddings—0
Improving Entity Linking by Modeling Latent Entity Type Information—0
Improving evaluation and optimization of MT systems against MEANT—0
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings—0
Improving Interpretability of Word Embeddings by Generating Definition and Usage—0
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in Algerian texts—0
Improving neural tagging with lexical information—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
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