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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 3101–3150 of 4002 papers

TitleStatusHype
Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations—0
Knowledge Distillation for Bilingual Dictionary Induction—0
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings—0
A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC—0
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics—0
Exploring Vector Spaces for Semantic Relations—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
AutoExtend: Combining Word Embeddings with Semantic Resources—0
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
NNVLP: A Neural Network-Based Vietnamese Language Processing ToolkitCode0
Handling Homographs in Neural Machine Translation—0
Probabilistic Relation Induction in Vector Space Embeddings—0
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language TasksCode0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Improved Answer Selection with Pre-Trained Word Embeddings—0
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier—0
Data Sets: Word Embeddings Learned from Tweets and General Data—0
Style2Vec: Representation Learning for Fashion Items from Style SetsCode0
Semi-supervised emotion lexicon expansion with label propagation and specialized word embeddingsCode0
Simple and Effective Dimensionality Reduction for Word EmbeddingsCode0
Making Sense of Word EmbeddingsCode0
Identifying Reference Spans: Topic Modeling and Word Embeddings help IR—0
Shortcut-Stacked Sentence Encoders for Multi-Domain InferenceCode0
A Syllable-based Technique for Word Embeddings of Korean Words—0
Risk Bounds for Transferring Representations With and Without Fine-Tuning—0
Zero-Inflated Exponential Family Embeddings—0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
Detecting Anxiety through RedditCode0
Adapting Pre-trained Word Embeddings For Use In Medical Coding—0
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words—0
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings—0
An Ontology-Based Method for Extracting and Classifying Domain-Specific Compositional Nominal Compounds—0
Event Detection Using Frame-Semantic Parser—0
Cross-Lingual Classification of Topics in Political Texts—0
Clinical Event Detection with Hybrid Neural Architecture—0
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks—0
Metaphor Detection in a Poetry Corpus—0
Automated Preamble Detection in Dictated Medical Reports—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation—0
Tracing armed conflicts with diachronic word embedding models—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Multilingual Semantic Parsing And Code-SwitchingCode0
An Artificial Language Evaluation of Distributional Semantic Models—0
Multi-Model and Crosslingual Dependency Analysis—0
Learning Word Representations with Regularization from Prior Knowledge—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
Parsing with Context Embeddings—0
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