Suchergebnisse

Berte Kanutte Aarflot lived in the Northwestern region of Norway in the 19th century. In this book, her great-great-grandson gives the first overview of her written work and puts it in the context of her time. Aarflot, a woman who left a big mark both in the religious sphere and in society, published over 600 pages of religious litterature. This is the 34th book in the series Kyrkjefag Profil. - "Berte Kanutte Aarflot satte tydelige spor etter seg både i kirken og i samfunnet generelt i første halvdel av 1800-tallet. Det er et særsyn at en enkel bondekvinne kunne vinne slik anseelse og betydning i sitt lokalmiljø på Sunnmøre, men innflytelsen hennes strakte seg også til store deler av Nordvestlandet, og langt utover
i landet ellers.

Med røtter i det haugianske miljøet i Ørsta og Volda sto hun frem som veileder og vekkerrøst i en brytningstid der nye nasjonale strømninger preget Norge. Hun var en foregangskvinne, både som selvlært forfatter og selvstendig formidler av kirkens budskap. Skriftene hennes omfattet mer enn 600 sider, og ble trykt i over tretti opplag i løpet av 1800-tallet.

I denne boken foreligger for første gang en grundig gjennomgang av arbeidene til Berte Kanutte Aarflot, med en presentasjon av hennes trosverden og religiøse tanker, satt inn i samtidens kontekst. Det er et samtidsbilde som maner til inspirasjon og ettertanke, også for lesere i vår tid.

Boka er nummer 34 i serien Kyrkjefag Profil."

Sarcasm is often used to humorously criticize something or hurt someone's feelings. Humans often have difficulty in recognizing sarcastic comments since we say the opposite of what we really mean. Thus, automatic sarcasm detection in textual data is one of the most challenging tasks in Natural Language Processing (NLP). It has also become a relevant research area due to its importance in the improvement of sentiment analysis. In this work, we explore several deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT) to address the task of sarcasm detection. While most research has been conducted using social media data, we evaluate our models using a news headlines dataset. To the best of our knowledge, this is the first study that applies BERT to detect sarcasm in texts that do not come from social media. Experiment results show that the BERT-based approach overcomes the state-of-the-art on this type of dataset. ; El sarcasmo se usa con frecuencia para realizar crítica o burla indirecta, a veces hiriendo los sentimientos de alguien. Algunas veces, las personas tienen dificultades para reconocer los comentarios sarcásticos, ya que decimos lo contrario de lo que realmente queremos decir. Por lo tanto, la detección automática de sarcasmo en textos es una de las tareas más complicadas en el Procesamiento del Lenguaje Natural (PLN). Además, se ha convertido en un área de investigación relevante debido a su importancia para mejorar el análisis de sentimientos. En este trabajo, exploramos varios modelos de aprendizaje profundo, como Bidirectional Long Short-Term Memory (BiLSTM) y Bidirectional Encoder Representations from Transformers (BERT) para abordar la tarea de detección de sarcasmo. Si bien la mayoría de los trabajos anteriores se han centrado en datasets construidos con textos de redes sociales, en este artículo, evaluamos nuestros modelos utilizando un dataset formado por titulares de noticias. Por tanto, este es el primer estudio que aplica BERT para detectar el sarcasmo en textos que no provienen de las redes sociales. Los resultados de los experimentos muestran que el enfoque basado en BERT supera el estado del arte en este tipo de conjunto de datos. ; This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research" (NLP4RARE-CM-UC3M), as well as in the line of "Excellence of University Professors" (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

This paper presents a monolingual BERT model for Galician. We follow the recent trend that shows that it is feasible to build robust monolingual BERT models even for relatively low-resource languages, while performing better than the well-known official multilingual BERT (mBERT). More particularly, we release two monolingual Galician BERT models, built using 6 and 12 transformer layers, respectively; trained with limited resources (~45 million tokens on a single GPU of 24GB). We then provide an exhaustive evaluation on a number of tasks such as POS-tagging, dependency parsing and named entity recognition. For this purpose, all these tasks are cast in a pure sequence labeling setup in order to run BERT without the need to include any additional layers on top of it (we only use an output classification layer to map the contextualized representations into the predicted label). The experiments show that our models, especially the 12-layer one, outperform the results of mBERT in most tasks. ; Este artículo presenta un modelo BERT monolingüe para el gallego. Nos basamos en la tendencia actual que ha demostrado que es posible crear modelos BERT monolingües robustos incluso para aquellos idiomas para los que hay una relativa escasez de recursos, funcionando éstos mejor que el modelo BERT multilingüe oficial (mBERT). Concretamente, liberamos dos modelos monolingües para el gallego, creados con 6 y 12 capas de transformers, respectivamente, y entrenados con una limitada cantidad de recursos (~45 millones de palabras sobre una única GPU de 24GB.) Para evaluarlos realizamos un conjunto exhaustivo de experimentos en tareas como análisis morfosintáctico, análisis sintáctico de dependencias o reconocimiento de entidades. Para ello, abordamos estas tareas como etiquetado de secuencias, con el objetivo de ejecutar los modelos BERT sin la necesidad de incluir ninguna capa adicional (únicamente se añade la capa de salida encargada de transformar las representaciones contextualizadas en la etiqueta predicha). Los experimentos muestran que nuestros modelos, especialmente el de 12 capas, mejoran los resultados de mBERT en la mayor parte de las tareas. ; This work has received funding from the European Research Council (ERC), which has funded this research under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from MINECO (ANSWER-ASAP, TIN2017-85160-C2-1-R), from Xunta de Galicia (ED431C 2020/11), from Centro de Investigación de Galicia `CITIC', funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01, and by Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), ERDF 2014-2020: Call ED431G 2019/04. DV is supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation. MG is supported by a Ramón y Cajal grant (RYC2019-028473-I).

Sarcasm is often used to humorously criticize something or hurt someone's feelings. Humans often have difficulty in recognizing sarcastic comments since we say the opposite of what we really mean. Thus, automatic sarcasm detection in textual data is one of the most challenging tasks in Natural Language Processing (NLP). It has also become a relevant research area due to its importance in the improvement of sentiment analysis. In this work, we explore several deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT) to address the task of sarcasm detection. While most research has been conducted using social media data, we evaluate our models using a news headlines dataset. To the best of our knowledge, this is the first study that applies BERT to detect sarcasm in texts that do not come from social media. Experiment results show that the BERT-based approach overcomes the state-of-the-art on this type of dataset. ; This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research"(NLP4RARE-CM-UC3M), as well as in the line of "Excellence of University Professors"(EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).

IN THIS INTERVIEW, HUMBERTO "BERT" CORONA--LABOR ORGANIZER SINCE THE 1930S, CHAMPION OF IMMIGRANT RIGHTS, EARLY LEADER OF CHICANO POLITICS, TEACHER, AND ORAL HISTORIAN-REMINDS US THAT THE US IS ONE OF ONLY TWO INDUSTRIALIZED CAPITALIST STATES IN THE WORLD WHICH BORDERS A THIRD WORLD NATION (SOUTH AFRICA IS THE OTHER). AS SUCH, IMMIGRANT WORKERS ARE AN INSEPARABLE PART OF THE US WORKFORCE; THEY ARE NOT "TEMPORARY" WORKERS, AS SOME WOULD LIKE TO THINK. MEXICAN NATIONALS AND MEXICAN AMERICANS HAVE LONG WORKED IN THE SHIPBUILDING, SHOEMAKING, FOOD PROCESSING, GARMENT AND OTHER INDUSTRIES, AS WELL AS IN AGRICULTURE, AND HAVE LED MANY OF THE STRUGGLES FOR UNIONIZATION IN THOSE INDUSTRIES. CORONA ALSO REMINDS US THAT, JUST AS EUROPEAN IMMIGRANTS IN THE LABOR MOVEMENT WERE SINGLED OUT FOR REPRESSION DURING THE PALMER RAIDS OF THE 1920S, SO WERE MEXICAN AND OTHER LATINO WORKERS SUBJECT TO REPRESSION DURING THE MCCARTHY ERA. IN OUR PRESENT CLIMATE OF HOSTILITY TOWARD UNDOCUMENTED WORKERS, THIS REPRESSION CONTINUES.

I would like to address myself to international outlaws and what domestic procedures are available to arrest their activities. While at first glance the nexus between domestic justice and international justice may seem tenuous, I wonder: Is it surprising that the same administration that is so insensate over the deprivation of the human rights of blacks in Southern Rhodesia is the same administration that proclaimed early in its tenure that if you have seen one slum you have pretty much seen them all, and hasn't visited another since? Is it surprising that the same administration that evidences so little concern over the political rights of the majority in Rhodesia is the same administration that "bugs" and sabotages the political process within the United States?