# Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit

## Documentation for package ‘udpipe’ version 0.8.11

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

&-method | Experimental and undocumented querying of syntax relationships
as.data.frame.udpipe_connlu | Convert the result of udpipe_annotate to a tidy data frame
as.matrix.cooccurrence | Convert the result of cooccurrence to a sparse matrix
as_conllu | Convert a data.frame to CONLL-U format
as_cooccurrence | Convert a matrix to a co-occurrence data.frame
as_fasttext | Combine labels and text as used in fasttext
as_phrasemachine | Convert Parts of Speech tags to one-letter tags which can be used to identify phrases based on regular expressions
as_word2vec | Convert a matrix of word vectors to word2vec format
brussels_listings | Brussels AirBnB address locations available at www.insideairbnb.com
brussels_reviews | Reviews of AirBnB customers on Brussels address locations available at www.insideairbnb.com
brussels_reviews_anno | Reviews of the AirBnB customers which are tokenised, POS tagged and lemmatised
brussels_reviews_w2v_embeddings_lemma_nl | An example matrix of word embeddings
cbind_dependencies | Add the dependency parsing information to an annotated dataset
cbind_morphological | Add morphological features to an annotated dataset
collocation | Extract collocations - a sequence of terms which follow each other
cooccurrence | Create a cooccurence data.frame
cooccurrence.character | Create a cooccurence data.frame
cooccurrence.cooccurrence | Create a cooccurence data.frame
cooccurrence.data.frame | Create a cooccurence data.frame
document_term_frequencies | Aggregate a data.frame to the document/term level by calculating how many times a term occurs per document
document_term_frequencies.character | Aggregate a data.frame to the document/term level by calculating how many times a term occurs per document
document_term_frequencies.data.frame | Aggregate a data.frame to the document/term level by calculating how many times a term occurs per document
document_term_frequencies_statistics | Add Term Frequency, Inverse Document Frequency and Okapi BM25 statistics to the output of document_term_frequencies
document_term_matrix | Create a document/term matrix
document_term_matrix.data.frame | Create a document/term matrix
document_term_matrix.default | Create a document/term matrix
document_term_matrix.DocumentTermMatrix | Create a document/term matrix
document_term_matrix.integer | Create a document/term matrix
document_term_matrix.matrix | Create a document/term matrix
document_term_matrix.numeric | Create a document/term matrix
document_term_matrix.simple_triplet_matrix | Create a document/term matrix
document_term_matrix.TermDocumentMatrix | Create a document/term matrix
dtm_align | Reorder a Document-Term-Matrix alongside a vector or data.frame
dtm_bind | Combine 2 document term matrices either by rows or by columns
dtm_cbind | Combine 2 document term matrices either by rows or by columns
dtm_chisq | Compare term usage across 2 document groups using the Chi-square Test for Count Data
dtm_colsums | Column sums and Row sums for document term matrices
dtm_conform | Make sure a document term matrix has exactly the specified rows and columns
dtm_cor | Pearson Correlation for Sparse Matrices
dtm_rbind | Combine 2 document term matrices either by rows or by columns
dtm_remove_lowfreq | Remove terms occurring with low frequency from a Document-Term-Matrix and documents with no terms
dtm_remove_sparseterms | Remove terms with high sparsity from a Document-Term-Matrix
dtm_remove_terms | Remove terms from a Document-Term-Matrix and keep only documents which have a least some terms
dtm_remove_tfidf | Remove terms from a Document-Term-Matrix and documents with no terms based on the term frequency inverse document frequency
dtm_reverse | Inverse operation of the document_term_matrix function
dtm_rowsums | Column sums and Row sums for document term matrices
dtm_sample | Random samples and permutations from a Document-Term-Matrix
dtm_svd_similarity | Semantic Similarity to a Singular Value Decomposition
dtm_tfidf | Term Frequency - Inverse Document Frequency calculation
keywords_collocation | Extract collocations - a sequence of terms which follow each other
keywords_phrases | Extract phrases - a sequence of terms which follow each other based on a sequence of Parts of Speech tags
keywords_rake | Keyword identification using Rapid Automatic Keyword Extraction (RAKE)
paste.data.frame | Concatenate text of each group of data together
phrases | Extract phrases - a sequence of terms which follow each other based on a sequence of Parts of Speech tags
predict.LDA | Predict method for an object of class LDA_VEM or class LDA_Gibbs
predict.LDA_Gibbs | Predict method for an object of class LDA_VEM or class LDA_Gibbs
predict.LDA_VEM | Predict method for an object of class LDA_VEM or class LDA_Gibbs
strsplit.data.frame | Obtain a tokenised data frame by splitting text alongside a regular expression
syntaxpatterns | Experimental and undocumented querying of syntax patterns
syntaxpatterns-class | Experimental and undocumented querying of syntax patterns
syntaxrelation | Experimental and undocumented querying of syntax relationships
syntaxrelation-class | Experimental and undocumented querying of syntax relationships
txt_collapse | Collapse a character vector while removing missing data.
txt_contains | Check if text contains a certain pattern
txt_context | Based on a vector with a word sequence, get n-grams (looking forward + backward)
txt_count | Count the number of times a pattern is occurring in text
txt_freq | Frequency statistics of elements in a vector
txt_grepl | Look up a multiple patterns and indicate their presence in text
txt_highlight | Highlight words in a character vector
txt_next | Get the n-th next element of a vector
txt_nextgram | Based on a vector with a word sequence, get n-grams (looking forward)
txt_overlap | Get the overlap between 2 vectors
txt_paste | Concatenate strings with options how to handle missing data
txt_previous | Get the n-th previous element of a vector
txt_previousgram | Based on a vector with a word sequence, get n-grams (looking backward)
txt_recode | Recode text to other categories
txt_recode_ngram | Recode words with compound multi-word expressions
txt_sample | Boilerplate function to sample one element from a vector.
txt_sentiment | Perform dictionary-based sentiment analysis on a tokenised data frame
txt_show | Boilerplate function to cat only 1 element of a character vector.
txt_tagsequence | Identify a contiguous sequence of tags as 1 being entity
udpipe | Tokenising, Lemmatising, Tagging and Dependency Parsing of raw text in TIF format
udpipe_accuracy | Evaluate the accuracy of your UDPipe model on holdout data
udpipe_annotate | Tokenising, Lemmatising, Tagging and Dependency Parsing Annotation of raw text
udpipe_annotation_params | List with training options set by the UDPipe community when building models based on the Universal Dependencies data
udpipe_download_model | Download an UDPipe model provided by the UDPipe community for a specific language of choice
udpipe_load_model | Load an UDPipe model
udpipe_read_conllu | Read in a CONLL-U file as a data.frame
udpipe_train | Train a UDPipe model
unique_identifier | Create a unique identifier for each combination of fields in a data frame
unlist_tokens | Create a data.frame from a list of tokens
|-method | Experimental and undocumented querying of syntax relationships