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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 3551–3600 of 4002 papers

TitleStatusHype
Morphological Priors for Probabilistic Neural Word Embeddings—0
New word analogy corpus for exploring embeddings of Czech wordsCode0
Exponential Family Embeddings—0
Recognizing Salient Entities in Shopping Queries—0
Learning Word Meta-Embeddings—0
Effects of Creativity and Cluster Tightness on Short Text Clustering Performance—0
The Kyoto University Cross-Lingual Pronoun Translation System—0
Embeddings for Word Sense Disambiguation: An Evaluation StudyCode0
A Domain Adaptation Regularization for Denoising Autoencoders—0
Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense LabelingCode0
Adjusting Word Embeddings with Semantic Intensity Orders—0
The TALP--UPC Spanish--English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System—0
Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings—0
Representing Support Verbs in FrameNet—0
Deep Neural Networks for Syntactic Parsing of Morphologically Rich Languages—0
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction—0
Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation—0
Cross-Lingual Word Representations via Spectral Graph EmbeddingsCode0
On Approximately Searching for Similar Word Embeddings—0
Evaluating Word Embeddings Using a Representative Suite of Practical Tasks—0
Evaluating word embeddings with fMRI and eye-tracking—0
Evaluation of acoustic word embeddings—0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
Measuring Semantic Similarity of Words Using Concept NetworksCode0
Chinese Zero Pronoun Resolution with Deep Neural Networks—0
Character-based Neural Machine Translation—0
Subsumption Preservation as a Comparative Measure for Evaluating Sense-Directed Embeddings—0
Modeling the Non-Substitutability of Multiword Expressions with Distributional Semantics and a Log-Linear Model—0
Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification—0
Unsupervised Text Segmentation Using Semantic Relatedness GraphsCode0
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time—0
bot.zen @ EmpiriST 2015 - A minimally-deep learning PoS-tagger (trained for German CMC and Web data)—0
Morphological Smoothing and Extrapolation of Word Embeddings—0
Using Embedding Masks for Word Categorization—0
Using Word Embeddings for Improving Statistical Machine Translation of Phrasal Verbs—0
Bilingual Embeddings and Word Alignments for Translation Quality Estimation—0
Bidirectional Recurrent Convolutional Neural Network for Relation Classification—0
A Word Embedding Approach to Identifying Verb-Noun Idiomatic Combinations—0
Multi-Modal Representations for Improved Bilingual Lexicon Learning—0
Sparsifying Word Representations for Deep Unordered Sentence Modeling—0
Phrase Representations for Multiword Expressions—0
Spanish NER with Word Representations and Conditional Random Fields—0
Roleo: Visualising Thematic Fit Spaces on the Web—0
On the Role of Seed Lexicons in Learning Bilingual Word Embeddings—0
Language Transfer Learning for Supervised Lexical Substitution—0
When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models—0
Singleton Detection using Word Embeddings and Neural Networks—0
Parameterized context windows in Random Indexing—0
SimpleNets: Quality Estimation with Resource-Light Neural Networks—0
An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods—0
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