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Application for Text Summarization
Mička, Jakub ; Zendulka, Jaroslav (referee) ; Bartík, Vladimír (advisor)
This work is focused on an implementation a web application, which is a tool for automatic English text summarization. In result, automatic text summarization is made by TextRank and Latent semantic analysis method. Both of these methods are improved by named entity recognition. The main benefit of this work is proving that using the named entity recognition with Latent semantic analysis and especially with TextRank method leads to creation of higher quality summaries. This quality of the summaries was verified by ROUGE metrics.
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Automatic Identification of Paraphrases
Otrusina, Lubomír ; Schwarz, Petr (referee) ; Smrž, Pavel (advisor)
Automatic paraphrase discovery is an important task in natural language processing. Many systems use paraphrases for improve performance e.g. systems for question answering, information retrieval or document summarization. In this thesis, we explain basic concepts e.g. paraphrase or paraphrase pattern. Next we propose some methods for paraphrase discovery from various resources. Subsequently we propose an unsupervised method for discovering paraphrase from large plain text based on context and keywords between NE pairs. In the end we explain evaluation metods in paraphrase discovery area and then we evaluate our system and compare it with similar systems.
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Entity Relationship Extraction
Šimečková, Zuzana ; Straka, Milan (advisor) ; Straňák, Pavel (referee)
Relationship extraction is the task of extracting semantic relationships between en- tities from a text. We create a Czech Relationship Extraction Dataset (CERED) using distant supervision on Wikidata and Czech Wikipedia. We detail the methodology we used and the pitfalls we encountered. Then we use CERED to fine-tune a neural network model for relationship extraction. We base our model on BERT - a linguistic model pre-trained on extensive unlabeled data. We demonstrate that our model performs well on existing English relationship datasets (Semeval 2010 Task 8, TACRED) and report the results we achieved on CERED. 1
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Application for Text Summarization
Mička, Jakub ; Zendulka, Jaroslav (referee) ; Bartík, Vladimír (advisor)
This work is focused on an implementation a web application, which is a tool for automatic English text summarization. In result, automatic text summarization is made by TextRank and Latent semantic analysis method. Both of these methods are improved by named entity recognition. The main benefit of this work is proving that using the named entity recognition with Latent semantic analysis and especially with TextRank method leads to creation of higher quality summaries. This quality of the summaries was verified by ROUGE metrics.
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Named Entity Recognition and Linking
Taufer, Pavel ; Straka, Milan (advisor) ; Kliegr, Tomáš (referee)
The goal of this master thesis is to design and implement a named entity recognition and linking algorithm. A part of this goal is to propose and create a knowledge base that will be used in the algorithm. Because of the limited amount of data for languages other than English, we want to be able to train our method on one language, and then transfer the learned parameters to other languages (that do not have enough training data). The thesis consists of description of available knowledge bases, existing methods and design and implementation of our own knowledge base and entity linking method. Our method achieves state of the art result on a few variants of the AIDA CoNLL-YAGO dataset. The method also obtains comparable results on a sample of Czech annotated data from the PDT dataset using the parameters trained on the English CoNLL dataset. Powered by TCPDF (www.tcpdf.org)
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Named Entity Normalization in Czech Texts
Kubát, Petr ; Vidová Hladká, Barbora (advisor) ; Popel, Martin (referee)
Named entities are collocations used to refer to real world objects in text. Named entity normalization is a process of generating the basic form for a given named entity. The thesis is focused on creating a rule- based procedure for named entity normalization in Czech texts. The process of designing individual rules is closely examined. Stress is laid on the fact that each rule is motivated by entities from real-world texts. Additionally, some aspects of Czech language syntax are analyzed in order to achieve the highest possible accuracy. Based on the theoretical description of the procedure, a normalization application is implemented, and its accuracy is evaluated by comparison with manually normalized entities. Together with already existing tools for automatic named entity recognition, it is possible to use this normalizer in other text processing tasks, such as machine translation, searching and categorization, etc. Powered by TCPDF (www.tcpdf.org)
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Automatic Identification of Paraphrases
Otrusina, Lubomír ; Schwarz, Petr (referee) ; Smrž, Pavel (advisor)
Automatic paraphrase discovery is an important task in natural language processing. Many systems use paraphrases for improve performance e.g. systems for question answering, information retrieval or document summarization. In this thesis, we explain basic concepts e.g. paraphrase or paraphrase pattern. Next we propose some methods for paraphrase discovery from various resources. Subsequently we propose an unsupervised method for discovering paraphrase from large plain text based on context and keywords between NE pairs. In the end we explain evaluation metods in paraphrase discovery area and then we evaluate our system and compare it with similar systems.
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