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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 2451–2500 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
Improving Optimization for Models With Continuous Symmetry Breaking—0
Improving Optimization in Models With Continuous Symmetry Breaking—0
Improving Translation of Out Of Vocabulary Words using Bilingual Lexicon Induction in Low-Resource Machine Translation—0
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings—0
Improving Vector Space Word Representations Using Multilingual Correlation—0
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses—0
Improving Word Alignment of Rare Words with Word Embeddings—0
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models—0
Improving Word Embeddings Using Kernel PCA—0
Improving Word Representations via Global Context and Multiple Word Prototypes—0
Improving Zero Shot Learning Baselines with Commonsense Knowledge—0
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter—0
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition—0
In-Context Former: Lightning-fast Compressing Context for Large Language Model—0
Incorporating Context into Language Encoding Models for fMRI—0
Incorporating Emoji Descriptions Improves Tweet Classification—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Incorporating Sub-Word Level Information in Language Invariant Neural Event Detection—0
Incorporating visual features into word embeddings: A bimodal autoencoder-based approach—0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification—0
Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions—0
Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings—0
Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test—0
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
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