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text2vec 0.6.5 (2023-10-16)

• fix test discovered with Matrix==1.6-2 release

text2vec 0.6.4 (2023-02-15)

• update dependency Matrix>=1.5-2, fixes #338

text2vec 0.6.2 (2022-09-11)

• removed test which is not needed with Matrix package v 1.5

text2vec 0.6

1. 2019-12-17
   • breaking change - removed construction of a vocabulary in parallel on windows
   • use rsparse package for SVD and GloVe factorizations
   • updated RWMD implementation (hopefully bug free)
2. 2018-09-10
   • breaking change - changed IDF formula - see #280 for details.
3. 2018-05-28
   • Added postag_lemma_tokenizer() (wrapper around udpipe::udpipe_annotate). Can be used as a drop-in replacement for more simple tokenizers in text2vec.
4. 2018-05-25
   • Made combine_vocabularies() part of public API - see #260 for details.
5. 2018-05-10
   • Added coherence() function for comprehensive coherence metrics. Thanks to Manuel Bickel ( @manuelbickel ) for conrtibution.
6. 2018-05-02
   • Fixed bug LSA model - document embeddings calculated as left singular vectors multiplied by singular values (not square root of values as before). Thanks to Sloane Simmons ( @singularperturbation )
   • Now fit_transform and transform methods in LDA model produce same results. Thanks to @jiunsiew for reporting. Also now LDA has n_iter_inference parameter. It controls number of the samples from converged distribution for document-topic inference. This leads to more robust document-topic probabilities (reduced variance). Default value is 10.
7. 2018-01-17
   • more numerically robust PMI, LFMD - thanks to @andland. Also adds iteration number iter to collocation_stat. iter shows iteration number when collocation stats (and counters) were calculated.

text2vec 0.5.1 [2018-01-10]

1. 2018-01-10
   • removed rank* columns from collocation_stat - were never used internally. Users can easily calculate ranks themselves
2. 2018-01-09
   • Added Bi-Normal Separation transformation, thanks to Pavel Shashkin ( @pshashk )
   • Added Dunning’s log-likelihood ratio for collocations, thanks to Chris Lee ( @Chrisss93 )
   • Early stopping for collocations learning
3. 2017-12-18
   • fixed several bugs #219 #217 #205
   • decreased number of dependencies - no more magrittr, uuid, tokenizers
   • removed distributed LDA which didn’t work correctly
4. 2017-10-18
   • Now tokenization is based on tokenizers and THE stringi packages.
   • models API follow mlapi package. No API changes on text2vec side - we just put abstract scikit-learn-like classes to a separate package in order to make them more reusable.

text2vec 0.5.0

1. 2017-06-12
   • Add additional filters to prune_vocabulary - filter by document counts
   • Clean up LSA, fixed transform method. Added option to use randomized SVD algorithm from irlba.
2. 2017-05-17
   • Imrove dist2 performamce for RWMD - incorporate ideas from gensim PR discussion.
3. 2017-05-17
   • API breaking change - vocabulary format change - now plain data.frame with meta-information in attributes (stopwords, ngram, number of docs, etc).
4. 2017-03-25
   • No more rely on RcppModules
   • API breaking change - removed lda_c from formats in DTM construction
   • added ifiles_parallel, itoken_parallel high-level functions for parallel computing
   • API breaking change chunks_numer parameter renamed to n_chunks
5. 2017-01-02
   • API breaking change - removed create_corpus from public API, moved co-occurence related optons to create_tcm from vecorizers
   • add ability to add custom weights for co-occurence statistics calculations
6. 2016-12-30
   • Noticeable speedup (1.5x) and even more noticeable improvement on memory usage (2x less!) for create_dtm, create_tcm . Now package relies on sparsepp library for underlying hash maps.
7. 2016-10-30
   • Collocations - detection of multi-word phrases using differend heuristics - PMI, gensim, LFMD.
8. 2016-10-20
   • Fixed bug in as.lda_c() function

text2vec 0.4.0

2016-10-03. See 0.4 milestone tags.

1. Now under GPL (>= 2) Licence
2. “immutable” iterators - no need to reinitialize them
3. unified models interface
4. New models: LSA, LDA, GloVe with L1 regularization
5. Fast similarity and distances calculation: Cosine, Jaccard, Relaxed Word Mover’s Distance, Euclidean
6. Better hadnling UTF-8 strings, thanks to @qinwf
7. iterators and models rely on R6 package

text2vec 0.3.0

1. 2016-01-13 fix for #46, thanks to @buhrmann for reporting
2. 2016-01-16 format of vocabulary changed.
   • do not keep doc_proportions. see #52.
   • add stop_words argument to prune_vocabulary. signature also was changed.
3. 2016-01-17 fix for #51. if iterator over tokens returns list with names, these names will be:
   • stored as attr(corpus, 'ids')
   • rownames in dtm
   • names for dtm list in lda_c format
4. 2016-02-02 high level function for corpus and vocabulary construction.
   • construction of vocabulary from list of itoken.
   • construction of dtm from list of itoken.
5. 2016-02-10 rename transformers
   • now all transformers starts with transform_* - more intuitive + simpler usage with autocompletion
6. 2016-03-29 (accumulated since 2016-02-10)
   • rename vocabulary to create_vocabulary.
   • new functions create_dtm, create_tcm.
   • All core functions are able to benefit from multicore machines (user have to register parallel backend themselves)
   • Fix for progress bars. Now they are able to reach 100% and ticks increased after computation.
   • ids argument to itoken. Simplifies assignement of ids to rows of DTM
   • create_vocabulary now can handle stopwords
   • see all updates here
7. 2016-03-30 more robust split_into() util.

text2vec 0.2.0 (2016-01-10)

First CRAN release of text2vec.

• Fast text vectorization with stable streaming API on arbitrary n-grams.
  • Functions for vocabulary extraction and management
  • Hash vectorizer (based on digest murmurhash3)
  • Vocabulary vectorizer
• GloVe algorithm word embeddings.
  • Fast term-co-occurence matrix factorization via parallel async AdaGrad.
• All core functions written in C++.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.