{ "_id": "6a1efedeb401979e7341a872", "Package": "udpipe", "Type": "Package", "Title": "Tokenization, Parts of Speech Tagging, Lemmatization and\nDependency Parsing with the 'UDPipe' 'NLP' Toolkit", "Version": "0.8.16", "Maintainer": "Jan Wijffels ", "Authors@R": "c(\nperson('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jwijffels@bnosac.be', comment = 'R wrapper'),\nperson('BNOSAC', role = 'cph', comment = 'R wrapper'),\nperson(\"Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic\", role = 'cph', comment = \"src/udpipe.cpp & src/udpipe.h\"),\nperson('Milan Straka', role = c('aut', 'cph'), email = 'straka@ufal.mff.cuni.cz', comment = \"src/udpipe.cpp & src/udpipe.h\"),\nperson('Jana Straková', role = c('ctb', 'cph'), email = 'strakova@ufal.mff.cuni.cz', comment = \"src/udpipe.cpp & src/udpipe.h\"))", "Description": "This natural language processing toolkit provides\nlanguage-agnostic 'tokenization', 'parts of speech tagging',\n'lemmatization' and 'dependency parsing' of raw text. Next to\ntext parsing, the package also allows you to train annotation\nmodels based on data of 'treebanks' in 'CoNLL-U' format as\nprovided at .\nThe techniques are explained in detail in the paper:\n'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with\nUDPipe', available at . The toolkit\nalso contains functionalities for commonly used data\nmanipulations on texts which are enriched with the output of\nthe parser. Namely functionalities and algorithms for\ncollocations, token co-occurrence, document term matrix\nhandling, term frequency inverse document frequency\ncalculations, information retrieval metrics (Okapi BM25),\nhandling of multi-word expressions, keyword detection (Rapid\nAutomatic Keyword Extraction, noun phrase extraction,\nsyntactical patterns) sentiment scoring and semantic similarity\nanalysis.", "License": "MPL-2.0", "URL": "https://bnosac.github.io/udpipe/en/index.html,\nhttps://github.com/bnosac/udpipe", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.2", "Repository": "https://bnosac.r-universe.dev", "Date/Publication": "2026-01-30 08:52:30 UTC", "RemoteUrl": "https://github.com/bnosac/udpipe", "RemoteRef": "HEAD", "RemoteSha": "3e8572cd7e6ee58b22cc30d29210a5f656b3b535", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-18 09:25:18 UTC", "User": "root" }, "Author": "Jan Wijffels [aut, cre, cph] (R wrapper),\nBNOSAC [cph] (R wrapper),\nInstitute of Formal and Applied Linguistics, Faculty of Mathematics and\nPhysics, Charles University in Prague, Czech Republic [cph]\n(src/udpipe.cpp & src/udpipe.h),\nMilan Straka [aut, cph] (src/udpipe.cpp & src/udpipe.h),\nJana Straková [ctb, cph] (src/udpipe.cpp & src/udpipe.h)", "MD5sum": "29f1ec16e7a9cdb1be7cfd279aad87e3", "_user": "bnosac", "_type": "src", "_file": "udpipe_0.8.16.tar.gz", "_fileid": "b75e2b1028929a983e8a722f7aecacf22e71ee0519506ff598674910326f085b", "_filesize": 3899846, "_sha256": "b75e2b1028929a983e8a722f7aecacf22e71ee0519506ff598674910326f085b", "_created": "2026-05-18T09:25:18.000Z", "_published": "2026-06-02T16:03:42.680Z", "_distro": "noble", "_jobs": [ { "job": 79115420726, "time": 210, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7054178414" }, { "job": 79115420411, "time": 255, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7054194112" }, { "job": 79115420668, "time": 215, "config": "linux-release-arm64", "r": 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"txt_previousgram", "txt_recode", "txt_recode_ngram", "txt_sample", "txt_sentiment", "txt_show", "txt_tagsequence", "udpipe", "udpipe_accuracy", "udpipe_annotate", "udpipe_download_model", "udpipe_load_model", "udpipe_read_conllu", "udpipe_train", "unique_identifier", "unlist_tokens" ], "_datasets": [ { "name": "brussels_listings", "title": "Brussels AirBnB address locations available at www.insideairbnb.com", "object": "brussels_listings", "file": "brussels_listings.RData", "class": [ "data.frame" ], "fields": [ "listing_id", "name", "host_id", "host_name", "neighbourhood_group", "neighbourhood", "latitude", "longitude", "room_type", "price", "minimum_nights", "number_of_reviews", "last_review", "reviews_per_month", "calculated_host_listings_count", "availability_365" ], "rows": 4903, "table": true, "tojson": true }, { "name": "brussels_reviews", "title": "Reviews of AirBnB customers on Brussels address locations available at www.insideairbnb.com", "object": "brussels_reviews", "file": "brussels_reviews.RData", "class": [ "data.frame" ], "fields": [ "id", "listing_id", "feedback", "language" ], "rows": 1500, "table": true, "tojson": true }, { "name": "brussels_reviews_anno", "title": "Reviews of the AirBnB customers which are tokenised, POS tagged and lemmatised", "object": "brussels_reviews_anno", "file": "brussels_reviews_anno.RData", "class": [ "data.frame" ], "fields": [ "doc_id", "language", "sentence_id", "token_id", "token", "lemma", "upos", "xpos" ], "rows": 96143, "table": true, "tojson": true }, { "name": "brussels_reviews_w2v_embeddings_lemma_nl", "title": "An example matrix of word embeddings", "object": "brussels_reviews_w2v_embeddings_lemma_nl", "file": "brussels_reviews_w2v_embeddings_lemma_nl.RData", "class": [ "matrix", "array" ], "fields": {}, "rows": 2687, "table": true, "tojson": true }, { "name": "udpipe_annotation_params", "title": "List with training options set by the UDPipe community when building models based on the Universal Dependencies data", "object": "udpipe_annotation_params", "file": "udpipe_annotation_params.RData", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "as_conllu", "title": "Convert a data.frame to CONLL-U format", "topics": [ "as_conllu" ] }, { "page": "as_cooccurrence", "title": "Convert a matrix to a co-occurrence data.frame", "topics": [ "as_cooccurrence" ] }, { "page": "as_fasttext", "title": "Combine labels and text as used in fasttext", "topics": [ "as_fasttext" ] }, { "page": "as_phrasemachine", "title": "Convert Parts of Speech tags to one-letter tags which can be used to identify phrases based on regular expressions", "topics": [ "as_phrasemachine" ] }, { "page": "as_word2vec", "title": "Convert a matrix of word vectors to word2vec format", "topics": [ "as_word2vec" ] }, { "page": "as.data.frame.udpipe_connlu", "title": "Convert the result of udpipe_annotate to a tidy data frame", "topics": [ "as.data.frame.udpipe_connlu" ] }, { "page": "as.matrix.cooccurrence", "title": "Convert the result of cooccurrence to a sparse matrix", "topics": [ "as.matrix.cooccurrence" ] }, { "page": "brussels_listings", "title": "Brussels AirBnB address locations available at www.insideairbnb.com", "topics": [ "brussels_listings" ] }, { "page": "brussels_reviews", "title": "Reviews of AirBnB customers on Brussels address locations available at www.insideairbnb.com", "topics": [ "brussels_reviews" ] }, { "page": "brussels_reviews_anno", "title": "Reviews of the AirBnB customers which are tokenised, POS tagged and lemmatised", "topics": [ "brussels_reviews_anno" ] }, { "page": "brussels_reviews_w2v_embeddings_lemma_nl", "title": "An example matrix of word embeddings", "topics": [ "brussels_reviews_w2v_embeddings_lemma_nl" ] }, { "page": "cbind_dependencies", "title": "Add the dependency parsing information to an annotated dataset", "topics": [ "cbind_dependencies" ] }, { "page": "cbind_morphological", "title": "Add morphological features to an annotated dataset", "topics": [ "cbind_morphological" ] }, { "page": "cooccurrence", "title": "Create a cooccurence data.frame", "topics": [ "cooccurrence", "cooccurrence.character", "cooccurrence.cooccurrence", "cooccurrence.data.frame" ] }, { "page": "document_term_frequencies", "title": "Aggregate a data.frame to the document/term level by calculating how many times a term occurs per document", "topics": [ "document_term_frequencies", "document_term_frequencies.character", "document_term_frequencies.data.frame" ] }, { "page": "document_term_frequencies_statistics", "title": "Add Term Frequency, Inverse Document Frequency and Okapi BM25 statistics to the output of document_term_frequencies", "topics": [ "document_term_frequencies_statistics" ] }, { "page": "document_term_matrix", "title": "Create a document/term matrix", "topics": [ "document_term_matrix", "document_term_matrix.data.frame", "document_term_matrix.default", "document_term_matrix.DocumentTermMatrix", "document_term_matrix.integer", "document_term_matrix.matrix", "document_term_matrix.numeric", "document_term_matrix.simple_triplet_matrix", "document_term_matrix.TermDocumentMatrix" ] }, { "page": "dtm_align", "title": "Reorder a Document-Term-Matrix alongside a vector or data.frame", "topics": [ "dtm_align" ] }, { "page": "dtm_bind", "title": "Combine 2 document term matrices either by rows or by columns", "topics": [ "dtm_bind", "dtm_cbind", "dtm_rbind" ] }, { "page": "dtm_chisq", "title": "Compare term usage across 2 document groups using the Chi-square Test for Count Data", "topics": [ "dtm_chisq" ] }, { "page": "dtm_colsums", "title": "Column sums and Row sums for document term matrices", "topics": [ "dtm_colsums", "dtm_rowsums" ] }, { "page": "dtm_conform", "title": "Make sure a document term matrix has exactly the specified rows and columns", "topics": [ "dtm_conform" ] }, { "page": "dtm_cor", "title": "Pearson Correlation for Sparse Matrices", "topics": [ "dtm_cor" ] }, { "page": "dtm_remove_lowfreq", "title": "Remove terms occurring with low frequency from a Document-Term-Matrix and documents with no terms", "topics": [ "dtm_remove_lowfreq" ] }, { "page": "dtm_remove_sparseterms", "title": "Remove terms with high sparsity from a Document-Term-Matrix", "topics": [ "dtm_remove_sparseterms" ] }, { "page": "dtm_remove_terms", "title": "Remove terms from a Document-Term-Matrix and keep only documents which have a least some terms", "topics": [ "dtm_remove_terms" ] }, { "page": "dtm_remove_tfidf", "title": "Remove terms from a Document-Term-Matrix and documents with no terms based on the term frequency inverse document frequency", "topics": [ "dtm_remove_tfidf" ] }, { "page": "dtm_reverse", "title": "Inverse operation of the document_term_matrix function", "topics": [ "dtm_reverse" ] }, { "page": "dtm_sample", "title": "Random samples and permutations from a Document-Term-Matrix", "topics": [ "dtm_sample" ] }, { "page": "dtm_svd_similarity", "title": "Semantic Similarity to a Singular Value Decomposition", "topics": [ "dtm_svd_similarity" ] }, { "page": "dtm_tfidf", "title": "Term Frequency - Inverse Document Frequency calculation", "topics": [ "dtm_tfidf" ] }, { "page": "keywords_collocation", "title": "Extract collocations - a sequence of terms which follow each other", "topics": [ "collocation", "keywords_collocation" ] }, { "page": "keywords_phrases", "title": "Extract phrases - a sequence of terms which follow each other based on a sequence of Parts of Speech tags", "topics": [ "keywords_phrases", "phrases" ] }, { "page": "keywords_rake", "title": "Keyword identification using Rapid Automatic Keyword Extraction (RAKE)", "topics": [ "keywords_rake" ] }, { "page": "paste.data.frame", "title": "Concatenate text of each group of data together", "topics": [ "paste.data.frame" ] }, { "page": "predict.LDA", "title": "Predict method for an object of class LDA_VEM or class LDA_Gibbs", "topics": [ "predict.LDA", "predict.LDA_Gibbs", "predict.LDA_VEM" ] }, { "page": "strsplit.data.frame", "title": "Obtain a tokenised data frame by splitting text alongside a regular expression", "topics": [ "strsplit.data.frame" ] }, { "page": "syntaxpatterns", "title": "Experimental and undocumented querying of syntax patterns", "topics": [ "syntaxpatterns", "syntaxpatterns-class" ] }, { "page": "syntaxrelation", "title": "Experimental and undocumented querying of syntax relationships", "topics": [ "&,logical,syntaxrelation-method", "&,syntaxrelation,logical-method", "syntaxrelation", "syntaxrelation-class", "|,logical,syntaxrelation-method", "|,syntaxrelation,logical-method" ] }, { "page": "txt_collapse", "title": "Collapse a character vector while removing missing data.", "topics": [ "txt_collapse" ] }, { "page": "txt_contains", "title": "Check if text contains a certain pattern", "topics": [ "txt_contains" ] }, { "page": "txt_context", "title": "Based on a vector with a word sequence, get n-grams (looking forward + backward)", "topics": [ "txt_context" ] }, { "page": "txt_count", "title": "Count the number of times a pattern is occurring in text", "topics": [ "txt_count" ] }, { "page": "txt_freq", "title": "Frequency statistics of elements in a vector", "topics": [ "txt_freq" ] }, { "page": "txt_grepl", "title": "Look up a multiple patterns and indicate their presence in text", "topics": [ "txt_grepl" ] }, { "page": "txt_highlight", "title": "Highlight words in a character vector", "topics": [ "txt_highlight" ] }, { "page": "txt_next", "title": "Get the n-th next element of a vector", "topics": [ "txt_next" ] }, { "page": "txt_nextgram", "title": "Based on a vector with a word sequence, get n-grams (looking forward)", "topics": [ "txt_nextgram" ] }, { "page": "txt_overlap", "title": "Get the overlap between 2 vectors", "topics": [ "txt_overlap" ] }, { "page": "txt_paste", "title": "Concatenate strings with options how to handle missing data", "topics": [ "txt_paste" ] }, { "page": "txt_previous", "title": "Get the n-th previous element of a vector", "topics": [ "txt_previous" ] }, { "page": "txt_previousgram", "title": "Based on a vector with a word sequence, get n-grams (looking backward)", "topics": [ "txt_previousgram" ] }, { "page": "txt_recode", "title": "Recode text to other categories", "topics": [ "txt_recode" ] }, { "page": "txt_recode_ngram", "title": "Recode words with compound multi-word expressions", "topics": [ "txt_recode_ngram" ] }, { "page": "txt_sample", "title": "Boilerplate function to sample one element from a vector.", "topics": [ "txt_sample" ] }, { "page": "txt_sentiment", "title": "Perform dictionary-based sentiment analysis on a tokenised data frame", "topics": [ "txt_sentiment" ] }, { "page": "txt_show", "title": "Boilerplate function to cat only 1 element of a character vector.", "topics": [ "txt_show" ] }, { "page": "txt_tagsequence", "title": "Identify a contiguous sequence of tags as 1 being entity", "topics": [ "txt_tagsequence" ] }, { "page": "udpipe", 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