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Document Embedding

6 papers with code ยท Methodology

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Discovering Topics With Neural Topic Models Built From PLSA Loss

ICLR 2020

The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents.

DOCUMENT EMBEDDING TOPIC MODELS

Doc2Vec on the PubMed corpus: study of a new approach to generate related articles

26 Nov 2019

The terminological indexing, words and stems contents of linked documents are highly similar between pmra and Doc2Vec PV-DBOW architecture.

DOCUMENT EMBEDDING

Discovering topics with neural topic models built from PLSA assumptions

25 Nov 2019

The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents.

DOCUMENT EMBEDDING TOPIC MODELS

Neural Embedding Allocation: Distributed Representations of Topic Models

10 Sep 2019

Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents.

DOCUMENT EMBEDDING TOPIC MODELS

Evaluating the Utility of Document Embedding Vector Difference for Relation Learning

18 Jul 2019

Recent work has demonstrated that vector offsets obtained by subtracting pretrained word embedding vectors can be used to predict lexical relations with surprising accuracy.

DOCUMENT EMBEDDING

STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings

ACL 2019

Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding.

DOCUMENT EMBEDDING SENTENCE EMBEDDING TEXT SUMMARIZATION

Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors

ACL 2019

To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises-Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors.

DOCUMENT EMBEDDING

STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings

ACL 2019

Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding.

DOCUMENT EMBEDDING SENTENCE EMBEDDING TEXT SUMMARIZATION

Terminology-based Text Embedding for Computing Document Similarities on Technical Content

5 Jun 2019

We propose in this paper a new, hybrid document embedding approach in order to address the problem of document similarities with respect to the technical content.

DOCUMENT EMBEDDING