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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 2676–2700 of 4002 papers

TitleStatusHype
Entropy-Based Subword Mining with an Application to Word Embeddings—0
Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text ClassificationCode0
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
Addressing Low-Resource Scenarios with Character-aware Embeddings—0
Using Language Learner Data for Metaphor DetectionCode0
Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings—0
Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia—0
Literal, Metphorical or Both? Detecting Metaphoricity in Isolated Adjective-Noun Phrases—0
Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning—0
Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection—0
Detecting Figurative Word Occurrences Using Recurrent Neural Networks—0
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings—0
Subword-level Composition Functions for Learning Word Embeddings—0
\#TeamINF at SemEval-2018 Task 2: Emoji Prediction in Tweets—0
UWB at SemEval-2018 Task 3: Irony detection in English tweets—0
Meaning\_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings—0
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge—0
EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction—0
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes—0
UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?—0
How Gender and Skin Tone Modifiers Affect Emoji Semantics in TwitterCode0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes—0
UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings—0
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention—0
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