This repository contains various scripts in Perl and Python that can be used as tools for Universal Dependencies. ============================== validate.py ============================== Reads a CoNLL-U file and verifies that it complies with the UD specification. It must be run with the language / treebank code and there must exist corresponding lists of treebank-specific features and dependency relations in order to check that they are valid, too. cat la_proiel-ud-train.conllu | validate.py --lang la_proiel ============================== check_sentence_ids.pl ============================== Reads CoNLL-U files from STDIN and verifies that every sentence has a unique id in the sent_id comment. All files of one treebank (repository) must be supplied at once in order to test treebank-wide id uniqueness. cat *.conllu | perl check_sentence_ids.pl ============================== conllu-stats.py conllu-stats.pl ============================== Reads a CoNLL-U file, collects various statistics and prints them. These two scripts, one in Python and the other in Perl, are independent of each other. The statistics they collect overlap but are not the same. The Perl script (conllu-stats.pl) was used to generate the stats.xml files in each data repository. ============================== mwtoken-stats.pl ============================== Reads a CoNLL-U file, collects statistics of multi-word tokens and prints them. cat *.conllu | perl mwtoken-stats.pl > mwtoken-stats.txt ============================== overlap.py ============================== Compares two CoNLL-U files and searches for sentences that occur in both (verbose duplicates of token sequences). Some treebanks, especially those where the original text had been acquired from the web, contained duplicate documents that were found at different addresses and downloaded twice. This tool helps to find out whether one of the duplicates fell in the training data and the other in development or test. The output has to be verified manually, as some “duplicates” are repetitions that occur naturally in the language (in particular short sentences such as “Thank you.”) The script can also help to figure out whether training-dev-test data split has been changed between two releases so that a previously training sentence is now in test or vice versa. That is something we want to avoid. ============================== find_duplicate_sentences.pl remove_duplicate_sentences.pl ============================== Similar to overlap.py but it works with the sentence-level attribute “text”. It remembers all sentences from STDIN or from input files whose names are given as arguments. The find script prints the duplicate sentences (ordered by length and number of occurrences) to STDOUT. The remove script works as a filter: it prints the CoNLL-U data from the input, except for the second and any subsequent occurrence of the duplicate sentences. ============================== conllu_to_conllx.pl ============================== Converts a file in the CoNLL-U format to the old CoNLL-X format. Useful with old tools (e.g. parsers) that require CoNLL-X as their input. Usage: perl conllu_to_conllx.pl < file.conllu > file.conll ============================== restore_conllu_lines.pl ============================== Merges a CoNLL-X and a CoNLL-U file, taking only the CoNLL-U-specific lines from CoNLL-U. Can be used to merge the output of an old parser that only works with CoNLL-X with the original annotation that the parser could not read. restore_conllu_lines.pl file-parsed.conll file.conllu ============================== conllu_to_text.pl ============================== Converts a file in the CoNLL-U format to plain text, word-wrapped to lines of 80 characters (but the output line will be longer if there is a word that is longer than the limit). The script can use either the sentence-level text attribute, or the word forms plus the SpaceAfter=No MISC attribute to output detokenized text. It also observes the sentence-level newdoc and newpar attributes, and the NewPar=Yes MISC attribute, if they are present, and prints an empty line between paragraphs or documents. Optionally, the script takes the language code as a parameter. Codes 'zh' and 'ja' will trigger a different word-wrapping algorithm that is more suitable for Chinese and Japanese. Usage: perl conllu_to_text.pl --lang zh < file.conllu > file.txt ============================== conll_convert_tags_to_uposf.pl ============================== This script takes the CoNLL columns CPOS, POS and FEAT and converts their combined values to the universal POS tag and features. You need Perl. On Linux, you probably already have it; on Windows, you may have to download and install Strawberry Perl. You also need the Interset libraries. Once you have Perl, it is easy to get them via the following (call "cpan" instead of "cpanm" if you do not have cpanm). cpanm Lingua::Interset Then use the script like this: perl conll_convert_tags_to_uposf.pl -f source_tagset < input.conll > output.conll The source tagset is the identifier of the tagset used in your data and known to Interset. Typically it is the language code followed by two colons and "conll", e.g. "sl::conll" for the Slovenian data of CoNLL 2006. See the tagset conversion tables at http://universaldependencies.github.io/docs/tagset-conversion/index.html for more tagset codes. IMPORTANT: The script assumes the CoNLL-X (2006 and 2007) file format. If your data is in another format (most notably CoNLL-U, but also e.g. CoNLL 2008/2009, which is not identical to 2006/2007), you have to modify the data or the script. Furthermore, you have to know something about the tagset driver (-f source_tagset above) you are going to use. Some drivers do not expect to receive three values joined by TAB characters. Some expect two values and many expect just a single tag, perhaps the one you have in your POS column. These factors may also require you to adapt the script to your needs. You may want to consult the documentation at https://metacpan.org/pod/Lingua::Interset. Go to Browse / Interset / Tagset, look up your language code and tagset name, then locate the list() function in the source code. That will give you an idea of what the input tags should look like (usually the driver is able to decode even some tags that are not on the list but have the same structure and feature values). ============================== check_files.pl ============================== This script must be run in a folder where all the data repositories (UD_*) are stored as subfolders. It checks the contents of the data repositories for various issues that we want to solve before a new release of UD is published.