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[pt] Como uma consequência natural da digitalização do sistema judiciário brasileiro, um grande e crescente número de documentos jurídicos tornou-se disponível na internet, especialmente decisões judiciais. Como ilustração, em 2020, o Judiciário brasileiro produziu 25 milhões de decisões. Neste mesmo ano, o Supremo Tribunal Federal (STF), a mais alta corte do judiciário brasileiro, produziu 99.5 mil decisões. Alinhados a esses valores, observamos uma demanda crescente por estudos voltados para a extração e exploração do conhecimento jurídico de grandes acervos de documentos legais. Porém, ao contrário do conteúdo de textos comuns (como por exemplo, livro, notícias e postagem de blog), o texto jurídico constitui um caso particular de uso de uma linguagem altamente convencionalizada. Infelizmente, pouca atenção é dada à extração de informações em domínios especializados, como textos legais. Do ponto de vista temporal, o Judiciário é uma instituição em constante evolução, que se molda para atender às demandas da sociedade. Com isso, o nosso objetivo é propor um processo confiável de extração de informações jurídicas de grandes acervos de documentos jurídicos, tomando como base o STF e as decisões monocráticas publicadas por este tribunal nos anos entre 2000 e 2018. Para tanto, pretendemos explorar a combinação de diferentes técnicas de Processamento de Linguagem Natural (PLN) e Extração de Informação (EI) no contexto jurídico. Da PLN, pretendemos explorar as estratégias automatizadas de reconhecimento de entidades nomeadas no domínio legal. Do ponto da EI, pretendemos explorar a modelagem dinâmica de tópicos utilizando a decomposição tensorial como ferramenta para investigar mudanças no raciocinio juridico presente nas decisões ao lonfo do tempo, a partir da evolução do textos e da presença de entidades nomeadas legais. Para avaliar a confiabilidade, exploramos a interpretabilidade do método empregado, e recursos visuais para facilitar a interpretação por parte de um especialista de domínio. Como resultado final, a proposta de um processo confiável e de baixo custo para subsidiar novos estudos no domínio jurídico e, também, propostas de novas estratégias de extração de informações em grandes acervos de documentos. / [en] As a natural consequence of the Brazilian Judicial System’s digitization, a large and increasing number of legal documents have become available on the Internet, especially judicial decisions. As an illustration, in 2020, 25 million decisions were produced by the Brazilian Judiciary. Meanwhile, the Brazilian Supreme Court (STF), the highest judicial body in Brazil, alone has produced 99.5 thousand decisions. In line with those numbers, we face a growing demand for studies focused on extracting and exploring the legal knowledge hidden in those large collections of legal documents. However, unlike typical textual content (e.g., book, news, and blog post), the legal text constitutes a particular case of highly conventionalized language. Little attention is paid to information extraction in specialized domains such as legal texts. From a temporal perspective, the Judiciary itself is a constantly evolving institution, which molds itself to cope with the demands of society. Therefore, our goal is to propose a reliable process for legal information extraction from large collections of legal documents, based on the STF scenario and the monocratic decisions published by it between 2000 and 2018. To do so, we intend to explore the combination of different Natural Language Processing (NLP) and Information Extraction (IE) techniques on legal domain. From NLP, we explore automated named entity recognition strategies in the legal domain. From IE, we explore dynamic topic modeling with tensor decomposition as a tool to investigate the legal reasoning changes embedded in those decisions over time through textual evolution and the presence of the legal named entities. For reliability, we explore the interpretability of the methods employed. Also, we add visual resources to facilitate interpretation by a domain specialist. As a final result, we expect to propose a reliable and cost-effective process to support further studies in the legal domain and, also, to propose new strategies for information extraction on a large collection of documents.

Factor models attribute stock returns to a linear combination of factors. A model with great explanatory power (R2) can be used to estimate the systematic risk of an investment. One of the most important factors is the industry which the company of the stock operates in. In commercial risk models this factor is often determined with a manually constructed stock classification scheme such as GICS. We present Natural Language Industry Scheme (NLIS), an automatic and multivalued classification scheme based on topic modeling. The topic modeling is performed on transcripts of company earnings calls and identifies a number of topics analogous to industries. We use non-negative matrix factorization (NMF) on a term-document matrix of the transcripts to perform the topic modeling. When set to explain returns of the MSCI USA index we find that NLIS consistently outperforms GICS, often by several hundred basis points. We attribute this to NLIS’ ability to assign a stock to multiple industries. We also suggest that the proportions of industry assignments for a given stock could correspond to expected future revenue sources rather than current revenue sources. This property could explain some of NLIS’ success since it closely relates to theoretical stock pricing. / Faktormodeller förklarar aktieprisrörelser med en linjär kombination av faktorer. En modell med hög förklaringsgrad (R2) kan användas föratt skatta en investerings systematiska risk. En av de viktigaste faktorerna är aktiebolagets industritillhörighet. I kommersiella risksystem bestäms industri oftast med ett aktieklassifikationsschema som GICS, publicerat av ett finansiellt institut. Vi presenterar Natural Language Industry Scheme (NLIS), ett automatiskt klassifikationsschema baserat på topic modeling. Vi utför topic modeling på transkript av aktiebolags investerarsamtal. Detta identifierar ämnen, eller topics, som är jämförbara med industrier. Topic modeling sker genom icke-negativmatrisfaktorisering (NMF) på en ord-dokumentmatris av transkripten. När NLIS används för att förklara prisrörelser hos MSCI USA-indexet finner vi att NLIS överträffar GICS, ofta med 2-3 procent. Detta tillskriver vi NLIS förmåga att ge flera industritillhörigheter åt samma aktie. Vi föreslår också att proportionerna hos industritillhörigheterna för en aktie kan motsvara förväntade inkomstkällor snarare än nuvarande inkomstkällor. Denna egenskap kan också vara en anledning till NLIS framgång då den nära relaterar till teoretisk aktieprissättning.

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Выпускная квалификационная работа на тему «Текст и текстовые категории в преподавании РКИ. Проект учебного пособия для филологов-иностранцев» включает 124 страницы основного текста, 2 таблицы, использованных источников – 89. Диссертация посвящена обучению русскому языку как иностранному. Объектом исследования является методический аспект изучения текстовых категории в преподавании русского как иностранного. Предметом исследования является обучение текстовых категорий в аспекте преподавания русского как иностранного, способствующего развитию навыков написания самостоятельных текстов, изучающего и поискового чтения. Цель работы – продемонстрировать как можно использовать текст и текстовые категории для развития культуры речи студентов, умений работать с аутентичными текстами. В первой главе раскрываются особенности текста и текстовых категорий в современной лингвистике текста, роль текста в преподавании русского как иностранного. Вторая глава представляет собой проект учебного пособия для иностранцев-филологов, в котором представлена структура работы с текстом и текстовыми категориями. Материалом исследования послужили работы отечественных и зарубежных ученых в области теории (лингвистики) текста, методики преподавания русского как иностранного. Диссертация предназначена для студентов, магистрантов филологического направления, преподавателям РКИ, читателям, которых интересует изучение русского языка. / Graduation qualification thesis on the topic «Text and text categories in teaching Russian as a Foreign Language. Project textbook for foreign students of philological faculty» includes 124 pages of the main text, 2 tables and 89 sources. The dissertation is devoted to teaching Russian as a foreign language. The object of the research is the methodological aspect of studying text categories in teaching Russian as a foreign language. The subject of the research is the teaching of text categories in the aspect of teaching Russian as a foreign language, which contributes to the development of skills of writing independent texts, studying and searching reading. The aim of the work demonstrates that text and text categories can be used to develop student’s speech culture and skills at working with authentic texts. The first chapter reveals the features of the text and text categories in the modern linguistics of the text, the role of the text in teaching Russian as a foreign language. The second chapter is a project textbook for foreign students of philological faculty, which presents the structure of working with text and text categories. The research material is based on the works of Russian and foreign scientists in the field of text theory (linguistics), methods of teaching Russian as a foreign language. The dissertation is intended for students and master degree of philological faculty, teachers of Russian as a foreign language, readers who are interested in studying Russian language.

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