Tokenization, Parts of Speech Tagging, Lemmatization and
Dependency Parsing with the 'UDPipe' 'NLP' Toolkit
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Documentation for package ‘udpipe’ version 0.8.11
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 |