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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 3001–3025 of 4002 papers

TitleStatusHype
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl—0
Improving Lexical Choice in Neural Machine TranslationCode0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction—0
Synonym Discovery with Etymology-based Word Embeddings—0
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models—0
Structured Embedding Models for Grouped DataCode0
KeyVec: Key-semantics Preserving Document Representations—0
Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity RecognitionCode0
Learning of Colors from Color Names: Distribution and Point EstimationCode0
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts—0
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings—0
On the Use of Machine Translation-Based Approaches for Vietnamese Diacritic Restoration—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
MetaLDA: a Topic Model that Efficiently Incorporates Meta informationCode0
Think Globally, Embed Locally --- Locally Linear Meta-embedding of WordsCode0
Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection—0
Leveraging Distributional Semantics for Multi-Label Learning—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Affective Neural Response Generation—0
StarSpace: Embed All The Things!Code0
Social Media Text Processing and Semantic Analysis for Smart Cities—0
Improving average ranking precision in user searches for biomedical research datasets—0
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings—0
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