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• Automatic Speech Recognition and Token boundary detection for Italian Speech

  1 resources

  Open Source code for the implementation of an Automatic Speech Recognition system for Italian speech. Can perform automated transcription as well as Speech-text alignment.

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• The CLASSLA-StanfordNLP model for named entity recognition of non-standard Serbian 1.0

  2 resources

  This model for named entity recognition of non-standard Serbian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200), the hr500k training corpus (http://hdl.handle.net/11356/1183), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240) and the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206). The training corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed.

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• NPSemRel

  1 resources

  NPSemrel is a tool for recognizing semantic roles into nominal Phrases.

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• ForFun 1.0

  2 resources

  ForFun is a database of linguistic forms and their syntactic functions built with the use of the multi-layer annotated corpora of Czech, the Prague Dependency Treebanks. The purpose of the Prague Database of Forms and Functions (ForFun) is to help the linguists to study the form-function relation, which we assume to be one of the principal tasks of both theoretical linguistics and natural language processing. A prototypical question to be asked is "What purposes does a preposition 'po' serve for" or "What are the linguistic means in the sentence that can express the meaning 'a destination of an action'?". There are almost 1500 distinct forms (besides the 'po' preposition) and 65 distinct functions (besides the 'destination').

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• CorPipe 23 multilingual CorefUD 1.2 model (corpipe23-corefud1.2-240906)

  2 resources

  The `corpipe23-corefud1.2-240906` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 23 <https://github.com/ufal/crac2023-corpipe>. It is released under the CC BY-NC-SA 4.0 license. The model is language agnostic (no corpus id on input), so it can be in theory used to predict coreference in any `mT5` language. However, the model expects empty nodes to be already present on input, predicted by the https://www.kaggle.com/models/ufal-mff/crac2024_zero_nodes_baseline/. This model was present in the CorPipe 24 paper as an alternative to a single-stage approach, where the empty nodes are predicted joinly with coreference resolution (via http://hdl.handle.net/11234/1-5672), an approach circa twice as fast but of slightly worse quality.

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• The CLASSLA-StanfordNLP model for lemmatisation of non-standard Slovenian 1.0

  2 resources

  The model for lemmatisation of non-standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and the Janes-Tag corpus (http://hdl.handle.net/11356/1238), using the Sloleks inflectional lexicon (http://hdl.handle.net/11356/1230). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The estimated F1 of the lemma annotations is ~98.86.

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• Universal Dependencies 2.12 models for UDPipe 2 (2023-07-17)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 131 treebanks of 72 languages of Universal Depenencies 2.12 Treebanks, created solely using UD 2.12 data (https://hdl.handle.net/11234/1-5150). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_212_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• TMODS:ENG-CZE -- query translation

  3 resources

  AMALACH project component TMODS:ENG-CZE; machine translation of queries from Czech to English. This archive contains models for the Moses decoder (binarized, pruned to allow for real-time translation) and configuration files for the MTMonkey toolkit. The aim of this package is to provide a full service for Czech->English translation which can be easily utilized as a component in a larger software solution. (The required tools are freely available and an installation guide is included in the package.) The translation models were trained on CzEng 1.0 corpus and Europarl. Monolingual data for LM estimation additionally contains WMT news crawls until 2013.

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• Liner2.5 rc3

  2 resources

  A framework for multitask sequence labeling dedicated for natural language processing tasks.

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• VIADAT

  2 resources

  This component integrates other VIADAT modules; together with VIADAT-REPO this composes the Virtual Assistant for accessing historical audiovisual data. The zip archive contains sources for the following modules: VIADAT, VIADAT-DEPOSIT, VIADAT-TEXT, VIADAT-ANNOTATE, VIADAT-ANALYZE, VIADAT-STAT, VIADAT-GIS and VIADAT-SEARCH. Developed in cooperation with ÚSD AV ČR and NFA.

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