Showing 3 of total 3 results (show query)
bnosac
doc2vec:Distributed Representations of Sentences, Documents and Topics
Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms, namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <arXiv:1405.4053>. The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering. It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps these document embeddings to a lower-dimensional space using the 'Uniform Manifold Approximation and Projection' (UMAP) clustering algorithm and finds dense areas in that space using a 'Hierarchical Density-Based Clustering' technique (HDBSCAN). These dense areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can be found which are representative of the topic. More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at <arXiv:2008.09470>.
Maintained by Jan Wijffels. Last updated 3 years ago.
doc2vecembeddingsnatural-language-processingparagraph2vecword2veccpp
69.8 match 48 stars 5.74 score 23 scripts
mlampros
textTinyR:Text Processing for Small or Big Data Files
It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.
Maintained by Lampros Mouselimis. Last updated 1 years ago.
bhboostcpp11processingrcpprcpparmadillotextopenblascppopenmp
9.2 match 38 stars 7.64 score 244 scripts 1 dependentsbnosac
word2vec:Distributed Representations of Words
Learn vector representations of words by continuous bag of words and skip-gram implementations of the 'word2vec' algorithm. The techniques are detailed in the paper "Distributed Representations of Words and Phrases and their Compositionality" by Mikolov et al. (2013), available at <arXiv:1310.4546>.
Maintained by Jan Wijffels. Last updated 1 years ago.
embeddingsnatural-language-processingword2veccpp
3.3 match 70 stars 8.36 score 227 scripts 6 dependents