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COMBO's models for Polish trained on the current version of Polish Dependency Bank using the HerBERT language model.

model for dependency parsing only model for part-of-speech tagging, morphological analysis, lemmatisation, and dependency parsing (dependency relation types without semantic extensions, e.g. adjunct instead of adjunct_temp) model for part-of-speech tagging, morphological analysis, lemmatisation, and dependency parsing (dependency relation types with semantic extensions, e.g. adjunct_temp)

model for part-of-speech tagging, morphological analysis, lemmatisation, and dependency parsing COMBO, MateParser and MaltParser. MaltParser is a transition-based dependency parser that uses a deterministic parsing algorithm. The deterministic parsing algorithm builds a dependency structure of an input sentence based on transitions (shift-reduce actions) predicted by a classifier. The classifier learns to predict the next transition given training data and the parse history. MateParser, in turn, is a graph-based parser that defines a space of well-formed candidate dependency trees for an input sentence, scores them given an induced parsing model, and selects the highest scoring dependency tree as a correct analysis of the input sentence. COMBO-pytorch model for dependency parsing only (with HerBERT-base embeddings), COMBO model for dependency parsing only COMBO model for part-of-speech tagging, lemmatisation, and dependency parsing COMBO model for part-of-speech tagging, lemmatisation, dependency parsing, and semantic role labelling COMBO model for (semantic) dependency parsing only MATE model for dependency parsing MaltParser model for dependency parsing

The PDB-UD-based models are trained on the current version of Polish Dependency Bank in Universal Dependencies format with the publicly available parsing systems – COMBO-pytorch, COMBO, UDPipe. COMBO-pytorch model for for part-of-speech tagging, lemmatisation, and dependency parsing (with HerBERT-base embeddings), COMBO-pytorch model for for part-of-speech tagging, lemmatisation, and dependency parsing (with HerBERT-large embeddings), COMBO-pytorch model for for part-of-speech tagging, lemmatisation, and dependency parsing (with fastText embeddings), COMBO model for part-of-speech tagging, lemmatisation, and dependency parsing COMBO model for part-of-speech tagging, lemmatisation, dependency parsing, and semantic role labelling UDPipe model for tokenisation, part-of-speech tagging, lemmatisation, and dependency parsing UDPipe model for tokenisation COMBO model for Polish (the model estimated for the PolEval 2018 competition) UDPipe model for Polish

COMBO's source code Beginner's tutorial (collab notebook) COMBO's performance on test sets for multiple languages from Universal Dependencies Web demos English Polish

See Dependency parsing section. 190115_COMBO_PDB_nosem.pkl – PDB-based COMBO model for part-of-speech tagging, lemmatisation, and dependency parsing 190115_COMBO_PDB_sem.pkl – PDB-based COMBO model for part-of-speech tagging, lemmatisation, dependency parsing and semantic role labelling NEW! PDB-based COMBO model compatible with the tagset of Morfeusz 2: 180912_PDBCOMBO.pkl MateParser NEW! PDB-based Mate model compatible with the tagset of Morfeusz 2: 180322_PDBMate.mdl PDB-based Mate model compatible with the tagset of Morfeusz: 170608_PDBMate.mdl MateParser 190125_MATE_PDB.model – PDB-based MateParser model for dependency parsing MaltParser 190125_MALT_PDB.mco – PDB-based MaltParser model for dependency parsing NEW! PDB-based MaltParser model compatible with the tagset of Morfeusz 2: 180322_PDBMalt.mco PDB-basd MaltParser model compatible with the tagset of Morfeusz: 170608_PDBMalt.mco

Model LAS UAS PDBMate 0.85 0.89 PDBMalt 0.82 0.86

The description of Polish dependency relations types is available on Polish dependency relation types. Dependency relation type Precision Recall F-Measure Mate Malt Mate Malt Mate Malt abbrev_punct 0.99 0.99 0.98 0.97 0.98 0.98 adjunct 0.89 0.73 0.92 0.77 0.82 0.75 adjunct_qt 0.74 0.51 0.76 0.58 0.75 0.55 aglt 1.00 0.98 1.00 0.98 0.98 0.98 app 0.75 0.58 0.69 0.52 0.72 0.55 aux 0.95 0.90 0.97 0.92 0.96 0.91 comp 0.90 0.85 0.87 0.82 0.88 0.84 comp_ag 0.95 0.90 0.96 0.91 0.94 0.90 comp_fin 0.87 0.75 0.86 0.79 0.87 0.77 comp_inf 0.95 0.91 0.96 0.90 0.93 0.90 cond 1.00 0.97 1.00 0.96 1.00 0.96 conjunct 0.85 0.71 0.82 0.65 0.82 0.68 imp 0.98 0.97 0.91 0.87 0.94 0.92 item 0.87 0.4 0.73 0.37 0.61 0.39 mwe 0.90 0.83 0.83 0.75 0.87 0.79 ne 0.87 0.78 0.73 0.64 0.76 0.70 neg 0.99 0.97 1.00 0.98 0.99 0.98 obj 0.89 0.81 0.91 0.86 0.89 0.83 obj_th 0.83 0.76 0.76 0.65 0.80 0.70 pd 0.86 0.77 0.80 0.72 0.87 0.74 pre_coord 0.86 0.76 0.78 0.55 0.82 0.64 punct 0.97 0.75 0.98 0.76 0.88 0.76 refl 0.99 0.96 0.99 0.96 0.99 0.96 root 0.91 0.80 0.91 0.81 0.94 0.80 subj 0.94 0.84 0.94 0.83 0.94 0.84

English Polish COMBO demo MaltParser demo in Multiservice NLP To parse a Polish text in Multiservice "Select predefined chain of actions": 5: Concraft, DependencyParser, input your text, and press the button "Run". To download the parser's output in CoNLL format, "Select output format:".

Polish NLP models are released under the CC BY-NC-SA 4.0 licence and by downloading them you accept the conditions of that licence.

The research was founded by SONATA 8 grant no 2014/15/D/HS2/03486 from the National Science Centre Poland and by the Polish Ministry of Science, Higher Education as part of the investment in the CLARIN-PL research infrastructure and by Digital Research Infrastructure for the Arts and Humanities DARIAH-PL. The computing was performed at Poznań Supercomputing and Networking Center.

Any questions, comments? Please send them to <alina AT SPAMFREE ipipan DOT waw DOT pl>.