PV-DM
E899024
distributed representation model
neural network model
paragraph vector model
unsupervised learning method
PV-DM is a neural network-based paragraph vector model that learns distributed representations of sentences and documents by predicting words from both context and document embeddings.
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
distributed representation model
ⓘ
neural network model ⓘ paragraph vector model ⓘ unsupervised learning method ⓘ |
| abbreviationOf | Paragraph Vector – Distributed Memory NERFINISHED ⓘ |
| advantage |
captures word order information in context
ⓘ
provides fixed-length vectors for variable-length texts ⓘ |
| application |
clustering of documents
ⓘ
document classification ⓘ information retrieval ⓘ sentiment analysis ⓘ |
| basedOn | neural language model ⓘ |
| canUse |
hierarchical softmax
ⓘ
negative sampling ⓘ |
| category |
document embedding methods
ⓘ
paragraph vectors ⓘ |
| comparedWith | bag-of-words models ⓘ |
| domain |
natural language processing
ⓘ
representation learning ⓘ |
| fullName | Paragraph Vector – Distributed Memory NERFINISHED ⓘ |
| inputUnit |
context window of words
ⓘ
document id ⓘ |
| inspiredBy | distributed memory model of word2vec ⓘ |
| introducedBy |
Quoc V. Le
NERFINISHED
ⓘ
Tomas Mikolov NERFINISHED ⓘ |
| introducedInPaper | Distributed Representations of Sentences and Documents NERFINISHED ⓘ |
| languageAgnostic | true ⓘ |
| learningParadigm | unsupervised ⓘ |
| learns |
distributed representations of documents
ⓘ
distributed representations of paragraphs ⓘ distributed representations of sentences ⓘ |
| optimizationMethod | stochastic gradient descent ⓘ |
| outputUnit | target word ⓘ |
| publicationYear | 2014 ⓘ |
| publishedAtConference | ICML 2014 NERFINISHED ⓘ |
| relatedTo |
PV-DBOW
ⓘ
word2vec ⓘ |
| representationType | dense vector ⓘ |
| supports | variable-length documents ⓘ |
| trainingDataRequirement | large unlabeled text corpora ⓘ |
| trainingObjective | predict words using context and document vectors ⓘ |
| uses |
context word embeddings
ⓘ
document embeddings ⓘ neural network classifier ⓘ |
| vectorSpace | continuous vector space ⓘ |
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.