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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 3351–3400 of 4002 papers

TitleStatusHype
Robust Training under Linguistic AdversityCode0
Literal or idiomatic? Identifying the reading of single occurrences of German multiword expressions using word embeddings—0
A Twitter Corpus and Benchmark Resources for German Sentiment Analysis—0
Attention Modeling for Targeted Sentiment—0
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization—0
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm—0
Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
Social Bias in Elicited Natural Language InferencesCode0
Grouping business news stories based on salience of named entities—0
Arabic Textual Entailment with Word Embeddings—0
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet—0
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language—0
An RNN-based Binary Classifier for the Story Cloze Test—0
Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data—0
Delexicalized Word Embeddings for Cross-lingual Dependency Parsing—0
Analyzing Semantic Change in Japanese Loanwords—0
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit—0
Reranking Translation Candidates Produced by Several Bilingual Word Similarity Sources—0
Real-Time Keyword Extraction from Conversations—0
How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis—0
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses—0
Efficient, Compositional, Order-sensitive n-gram EmbeddingsCode0
Lexical Simplification with Neural Ranking—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
Word Sense Filtering Improves Embedding-Based Lexical Substitution—0
Semantic Similarity of Arabic Sentences with Word Embeddings—0
Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources—0
Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content MeasurementCode0
Learning to Negate Adjectives with Bilinear Models—0
Elucidating Conceptual Properties from Word Embeddings—0
Modelling metaphor with attribute-based semantics—0
Centroid-based Text Summarization through Compositionality of Word EmbeddingsCode0
Nonsymbolic Text Representation—0
The Language of Place: Semantic Value from Geospatial Context—0
Automatic Argumentative-Zoning Using Word2vecCode0
Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?—0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
Dynamic Bernoulli Embeddings for Language EvolutionCode0
Story Cloze Ending Selection Baselines and Data Examination—0
What can you do with a rock? Affordance extraction via word embeddings—0
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram FeaturesCode0
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use—0
Sound-Word2Vec: Learning Word Representations Grounded in Sounds—0
A Comparative Study of Word Embeddings for Reading Comprehension—0
Dynamic Word Embeddings for Evolving Semantic DiscoveryCode0
Dynamic Word EmbeddingsCode0
Use Generalized Representations, But Do Not Forget Surface Features—0
LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and Vector Representations—0
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