![paragraph vector code paragraph vector code](https://scoreintl.org/wp-content/uploads/2019/12/DSC_3447-1-1024x683.jpg)
We show that binary paragraph vectors outperform autoencoder-based binary codes, despite using fewer bits. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval.
Paragraph vector code mods#
Cite (Informal): Binary Paragraph Vectors (Grzegorczyk & Kurdziel, 2017) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = "Binary Paragraph Vectors",īooktitle = "Proceedings of the 2nd Workshop on Representation Learning for Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. Association for Computational Linguistics.
![paragraph vector code paragraph vector code](https://i.pinimg.com/originals/ab/bf/89/abbf89b96ea1e61cfcf450eb202e33b5.png)
In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 121–130, Vancouver, Canada. | WS SIG: SIGREP Publisher: Association for Computational Linguistics Note: Pages: 121–130 Language: URL: DOI: 10.18653/v1/W17-2615 Bibkey: grzegorczyk-kurdziel-2017-binary Cite (ACL): Karol Grzegorczyk and Marcin Kurdziel. Anthology ID: W17-2615 Volume: Proceedings of the 2nd Workshop on Representation Learning for NLP Month: August Year: 2017 Address: Vancouver, Canada Venues: RepL4NLP This model can be used to rapidly retrieve a short list of highly relevant documents from a large document collection. Finally, we present a model that simultaneously learns short binary codes and longer, real-valued representations. Results from these experiments indicate that binary paragraph vectors can capture semantics relevant for various domain-specific documents. We also evaluate their precision in transfer learning settings, where binary codes are inferred for documents unrelated to the training corpus.
![paragraph vector code paragraph vector code](https://image.freepik.com/free-photo/golden-paragraph-symbol_103577-2575.jpg)
The obtained preliminary results on a set of two metrics are encouraging and may stimulate further research in this area.Abstract Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. The resulting system, ParVecMF, is compared to a ratings' matrix factorization approach on a reference dataset. Subsequently this information is fused along with the rating scores in a probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. Initially, a neural language processing model and more specifically the paragraph vector model is used to encode textual user reviews of variable length into feature vectors of fixed length. In this paper, a novel approach to incorporating the aforementioned models into the recommendation process is presented. Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data, with the actual textual description of items serving only as side information. In addition to the rating scores, those systems are enriched with textual evaluations of items by the users. Review-based recommender systems have gained noticeable ground in recent years.