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

PDB-UD-trained dependency parsing models for Polish

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

Parsing performance

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

10-fold cross-validation (avg.)

Precision, recall and f-score of individual dependency relations (avg.)

The description of Polish dependency relations types is available on Polish dependency relation types.

COMBO demos

• 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:".

Alina Wróblewska. Polish Dependency Parser Trained on an Automatically Induced Dependency Bank. Ph.D. dissertation, Institute of Computer Science, Polish Academy of Sciences, Warsaw, 2014.

Alina Wróblewska. Polish dependency bank. Linguistic Issues in Language Technology, 7(1), 2012.