Findings of the Shared Task on Multilingual Coreference Resolution

This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams; in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages)

Editor for creating gamebooks

Herní knihy jsou takové knihy, ve kterých čtenář řídí dějovou linii příběhu. Tato práce se zabývá možnostmi přesunutí klasických herních knihy do elektronické podoby. Je zaměřena na tvorbu herního systému, který dokáže obsáhnout komplexnost těchto děl, dále pak na vývoj programu - editoru, ve kterém bude možné tyto mnohdy rozsáhlé knihy vytvářet, editovat a následně uložit pro distribuci. V první části jsou analyzovány prvky, které jsou v tomto oboru používány, následně je popsán mnou vytvořený systém. Druhá část popisuje návrh editoru a způsob jeho implementace včetně ověření jeho funkcionality.ObhájenoIn gamebooks the reader is the one who controls the storyline. This work deals with the possibilities of transforming the classic gaming books into electronic form. It is focused on creating a game system, which is able to capture the complexity of those works, as well as the development of the program - editor where is possible to create, edit and then save those often large books for distribution. The first part analyzes the elements that are used in this field and describes the system created by me. The second part describes the design of the editor and the manner of its implementations, including verification of its functionality

Automatic extraction of Internet forum posts

Internet je velice rychle rostoucí médium. Stává se více žádané data na něm obsažená zpracovávat automaticky. Tato práce se zabývá extrakcí informací z webových zdrojů, především z webových diskuzních fór. Pojednává o tomto oboru a zkoumá existující systémy. Následně jsou tyto poznatky aplikovány a je navrhnut systém, který tento úkol plní bez zásahu člověka. Dále jsou použity metody strojového učení a analýzy přirozeného jazyka k označení významu získaných dat.ObhájenoInternet is very quickly growing medium. It is becoming more requisite to process data contained therein automatically. This work deals with extraction of informations from web sources, especially from the web discussion forums. It discuss this discipline and examines existing systems. Then is this knowledge aplicated and is designed a system, which does this job without a human intervation. There are also used methods of machine learning and of analysis of natural language to designation of meaning of acquired dat

Natural Language Generation

GS-2019-018 Processing of heterogeneousdata and its specialized applicationsComputational systems use natural language for communication with humans moreoften in the last years. This work summarises state-of-the-art approaches in thefield of generative models, especially in the text domain. It offers a complex study ofspecific problems known from this domain and related ones like adversarial training,reinforcement learning, artificial neural networks, etc. It also addresses the usageof these models in the context of non-generative approaches and the possibility ofcombining both

Hluboké učení pro textová data na mobilních zařízeních.

With the rise of Artificial Intelligence (AI), it is becoming a significant phenomenon in our lives. As with many other powerful tools, AI brings many advantages but many risks as well. Predictions and automation can significantly help in our everyday lives. However, sending our data to servers for processing can severely hurt our privacy. In this paper, we describe experiments designed to find out whether we can enjoy the benefits of AI in the privacy of our mobile devices. We focus on text data since such data are easy to store in large quantities for mining by third parties. We measure the performance of deep learning methods in terms of accuracy (when compared to fully-fledged server models) and speed (number of text documents processed in a second). We conclude our paper with findings that with few relatively small modifications, mobile devices can process hundreds to thousands of documents while leveraging deep learning models.Jako každá mocný nástroj, přináší spoustu výhod ale také spoustu risků. Predikce a automatizace může výrazně pomoci v našich každodenních životech. Odesílání uživatelských dat na server k procesování může narušit jejich soukromí. Soustředili jsme se na textová data a provedli několik experimentů, abychom ověřili, zda je odesílání dat na výpočetní servery nutností v době výkonných mobilních zařízeních

Deep Learning for Text Data on Mobile Devices

With the rise of Artificial Intelligence (AI), it is becoming a significant phenomenon in our lives. As with many other powerful tools, AI brings many advantages but many risks as well. Predictions and automation can significantly help in our everyday lives. However, sending our data to servers for processing can severely hurt our privacy. In this paper, we describe experiments designed to find out whether we can enjoy the benefits of AI in the privacy of our mobile devices. We focus on text data since such data are easy to store in large quantities for mining by third parties. We measure the performance of deep learning methods in terms of accuracy (when compared to fully-fledged server models) and speed (number of text documents processed in a second). We conclude our paper with findings that with few relatively small modifications, mobile devices can process hundreds to thousands of documents while leveraging deep learning models