Tokenizer, POS Tagger, Lemmatizer and Parser models for 99 treebanks of 63 languages of Universal Depenencies 2.6 Treebanks, created solely using UD 2.6 data (https://hdl.handle.net/11234/1-3226). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_26_models .
To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .
Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding.
The current version of the UDer collection contains eleven harmonized resources covering eleven different languages.
Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding. The current version of the UDer collection contains twenty-seven harmonized resources covering twenty different languages.
Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding. The current version of the UDer collection contains thirty-one harmonized resources covering twenty-one different languages.
Universal Segmentations (UniSegments) is a collection of lexical resources capturing morphological segmentations harmonised into a cross-linguistically consistent annotation scheme for many languages. The annotation scheme consists of simple tab-separated columns that stores a word and its morphological segmentations, including pieces of information about the word and the segmented units, e.g., part-of-speech categories, type of morphs/morphemes etc. The current public version of the collection contains 38 harmonised segmentation datasets covering 30 different languages.
We release a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the both plain and tagged corpora. and it is supported by the MosesCore project sponsored by the European Commission’s Seventh Framework Programme (Grant Number 288487).
The Valency Lexicon of Czech Verbs, Version 2.5 (VALLEX 2.5), is a collection of linguistically annotated data and documentation, resulting from an attempt at formal description of valency frames of Czech verbs. VALLEX 2.5 has been developed at the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague.
VALLEX 2.5 provides information on the valency structure (combinatorial potential) of verbs in their particular senses - there are roughly 2,730 lexeme entries containing together around 6,460 lexical units ("senses"). and LC 536 - Center for Computational Linguistics, 1ET100300517 and 1ET101120503.
VALLEX 3.0 provides information on the valency structure (combinatorial potential) of verbs in their particular senses, which are characterized by glosses and examples. VALLEX 3.0 describes almost 4 600 Czech verbs in more than 10 800 lexical units, i.e., given verbs in the given senses.
VALLEX 3.0 is a is a collection of linguistically annotated data and documentation, resulting from an attempt at formal description of valency frames of Czech verbs. In order to satisfy different needs of different potential users, the lexicon is distributed (i) in a HTML version (the data allows for an easy and fast navigation through the lexicon) and (ii) in a machine-tractable form as a single XML file, so that the VALLEX data can be used in NLP applications.
VALLEX 4.0 provides information on the valency structure (combinatorial potential) of verbs in their particular senses; each sense is by a gloss and examples. VALLEX 4.0 describes almost 4 700 Czech verbs in more than 11 000 lexical units, i.e., given verbs in the given senses. VALLEX 4.0 is a is a collection of linguistically annotated data and documentation, resulting from an attempt at formal description of valency frames of Czech verbs. In order to satisfy different needs of different potential users, the lexicon is distributed (i) in a HTML version (the data allows for an easy and fast navigation through the lexicon) and (ii) in a machine-tractable form, so that the VALLEX data can be used in NLP applications. VALLEX 4.0 provides (in addition to information from previous versions) also characteristics of verbs expressing reciprocity and reflexivity.
The data is provided in two formats: XML and JSON.
VALLEX 4.5 provides information on the valency structure (combinatorial potential) of Czech verbs in their particular senses (almost 4 700 verbs in more than 11 080 lexical units, supplemented with more than 290 nouns in more than 350 lexical units forming complex predicates with light verbs). VALLEX 4.5 is an enhanced successor of VALLEX 3.0, 3.5, and 4.0. In addition to the information stored there, VALLEX 4.5 provides a detailed description of reflexive verbs, i.e., verbs with the reflexive "se" or "si" as an obligatory part of their verb lexemes. VALLEX 4.5 covers 1 525 reflexive verbs in 1 545 lexical units (2 501 when aspectual counterparts counted separately). In order to satisfy different needs of different potential users, the lexicon is distributed (i) online in a HTML version (the data allows for an easy and fast navigation through the lexicon) and (ii) in this distribution in a machine-tractable form, so that the VALLEX data can be used in NLP applications.