Phonological neighborhood density is known to influence lexical access, speech production as well as perception processes. Lexical competition is thought to be the central concept from which the neighborhood effect emanates: highly competitive neighborhoods are characterized by large degrees of phonemic co-activation, which can delay speech recognition and facilitate speech production. The present study investigates phonetic learning in English as a foreign language in relation to phonological neighborhood density and onset density to see whether dense or sparse neighborhoods are more conducive to the incorporation of novel phonetic detail. In addition, the effect of voice-contrasted minimal pairs (bat-pat) is explored. Results indicate that sparser neighborhoods with weaker lexical competition provide the most optimal phonological environment for phonetic learning. Moreover, novel phonetic details are incorporated faster in neighborhoods without minimal pairs. Results indicate that lexical competition plays a role in the dissemination of phonetic updates in the lexicon of foreign language learners.
English model for NameTag, a named entity recognition tool. The model is trained on CoNLL-2003 training data. Recognizes PER, ORG, LOC and MISC named entities. Achieves F-measure 84.73 on CoNLL-2003 test data.
English models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from Morphium and SCOWL (Spell Checker Oriented Word Lists), the PoS tagger is trained on WSJ (Wall Street Journal). and This work has been using language resources developed and/or stored and/or distributed by the LINDAT/CLARIN project of the Ministry of Education of the Czech Republic (project LM2010013).
The morphological POS analyzer development was supported by grant of the Ministry of Education, Youth and Sports of the Czech Republic No. LC536 "Center for Computational Linguistics". The morphological POS analyzer research was performed by Johanka Spoustová (Spoustová 2008; the Treex::Tool::EnglishMorpho::Analysis Perl module). The lemmatizer was implemented by Martin Popel (Popel 2009; the Treex::Tool::EnglishMorpho::Lemmatizer Perl module). The lemmatizer is based on morpha, which was released under LGPL licence as a part of RASP system (http://ilexir.co.uk/applications/rasp).
The tagger algorithm and feature set research was supported by the projects MSM0021620838 and LC536 of Ministry of Education, Youth and Sports of the Czech Republic, GA405/09/0278 of the Grant Agency of the Czech Republic and 1ET101120503 of Academy of Sciences of the Czech Republic. The research was performed by Drahomíra "johanka" Spoustová, Jan Hajič, Jan Raab and Miroslav Spousta.
The file contains the charts, tables and figures serving to delineate the metaphor-metonymy cognitive mechanism behind English denominal verbs. The data was obtained by questionnaires and interviews, which was then documented into charts and tables. Figures submitted mainly provide clear outline and concise outline of the metaphor-metonymy models of denominalization.
The FERNET-C5 is a monolingual BERT language representation model trained from scratch on the Czech Colossal Clean Crawled Corpus (C5) data - a Czech mutation of the English C4 dataset. The training data contained almost 13 billion words (93 GB of text data). The model has the same architecture as the original BERT model, i.e. 12 transformation blocks, 12 attention heads and the hidden size of 768 neurons. In contrast to Google’s BERT models, we used SentencePiece tokenization instead of the Google’s internal WordPiece tokenization.
More details can be found in README.txt. Yet more detailed description is available in https://arxiv.org/abs/2107.10042
The same models are also released at https://huggingface.co/fav-kky/FERNET-C5
The book [1] contains spelling rules classified into ten categories, each category containing many rules. This XML file presents our implemented rules classified with six category tags, as is the case in the book. We implemented 24 rules since the remaining rules require diacritical and morphological analysis that are outside the scope of our present work.
References:
[1] Dr.Fahmy Al-Najjar, 'Spelling rules in ten easy lessons', Al Kawthar Library,2008. Available: https://www.alukah.net/library/0/53498/%D9%82%D9%88%D8%A7%D8%B9%D8%AF-%D8%A7%D9%84%D8%A5%D9%85%D9%84%D8%A7%D8%A1-%D9%81%D9%8A-%D8%B9%D8%B4%D8%B1%D8%A9-%D8%AF%D8%B1%D9%88%D8%B3-%D8%B3%D9%87%D9%84%D8%A9-pdf/
NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
Experimental materials, data and R scripts used in the paper "Garden-path sentences and the diversity of their
(mis)representations" (Ceháková - Chromý, 2023).
RobeCzech is a monolingual RoBERTa language representation model trained on Czech data. RoBERTa is a robustly optimized Transformer-based pretraining approach. We show that RobeCzech considerably outperforms equally-sized multilingual and Czech-trained contextualized language representation models, surpasses current state of the art in all five evaluated NLP tasks and reaches state-of-theart results in four of them. The RobeCzech model is released publicly at https://hdl.handle.net/11234/1-3691 and https://huggingface.co/ufal/robeczech-base, both for PyTorch and TensorFlow.