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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Sörlin, Sverker;

    Part of book: ISBN 978-1-009-10023-6QC 20221219

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från K...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Publikationer från KTH
    Part of book or chapter of book . 2022
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1017/978100...
    Part of book or chapter of book . 2022
    Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Evholt, David; Larsson, Oscar;

    Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från K...arrow_drop_down
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Jonas Sjöbergh; Viggo Kann;

    We present an online API to access a number of Natural Language Processing services developed at KTH. The services work on Swedish text. They include tokenization, part-of-speech tagging, shallow parsing, compound word analysis, word inflection, lemmatization, spelling error detection and correction, grammar checking, and more. The services can be accessed in several ways, including a RESTful interface, direct socket communication, and premade Web forms. The services are open to anyone. The source code is also freely available making it possible to set up another server or run the tools locally. We have also evaluated the performance of several of the services and compared them to other available systems. Both the precision and the recall for the Granska grammar checker are higher than for both Microsoft Word and Google Docs. The evaluation also shows that the recall is greatly improved when combining all the grammar checking services in the API, compared to any one method, and combining services is made easy by the API. QC 20230328

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ https://ecp.ep.liu.s...arrow_drop_down
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    https://ecp.ep.liu.se/index.ph...
    Conference object
    License: cc-by
    Data sources: UnpayWall
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Stahre, Mattias;

    The use of Deep Learning methods for Document Understanding has been embraced by the research community in recent years. A requirement for Deep Learning methods and especially Transformer Networks, is access to large datasets. The objective of this thesis was to evaluate a state-of-the-art model for Document Layout Analysis on a public and custom dataset. Additionally, the objective was to build a pipeline for building a dataset specifically for Visually Rich Documents. The research methodology consisted of a literature study to find the state-of-the-art model for Document Layout Analysis and a relevant dataset used to evaluate the chosen model. The literature study also included research on how existing datasets in the domain were collected and processed. Finally, an evaluation framework was created. The evaluation showed that the chosen multi-modal transformer network, LayoutLMv2, performed well on the Docbank dataset. The custom build dataset was limited by class imbalance, although good performance for the larger classes. The annotator tool and its auto-tagging feature performed well and the proposed pipelined showed great promise for creating datasets with Visually Rich Documents. In conclusion, this thesis project answers the research questions and suggests two main opportunities. The first is to encourage others to build datasets with Visually Rich Documents using a similar pipeline to the one presented in this paper. The second is to evaluate the possibility of creating the visual token information for LayoutLMv2 as part of the transformer network rather than using a separate CNN. Användningen av Deep Learning-metoder för dokumentförståelse har anammats av forskarvärlden de senaste åren. Ett krav för Deep Learning-metoder och speciellt Transformer Networks är tillgång till stora datamängder. Syftet med denna avhandling var att utvärdera en state-of-the-art modell för analys av dokumentlayout på en offentligt tillgängligt dataset. Dessutom var målet att bygga en pipeline för att bygga en dataset specifikt för Visuallt Rika Dokument. Forskningsmetodiken bestod av en litteraturstudie för att hitta modellen för Document Layout Analys och ett relevant dataset som användes för att utvärdera den valda modellen. Litteraturstudien omfattade också forskning om hur befintliga dataset i domänen samlades in och bearbetades. Slutligen skapades en utvärderingsram. Utvärderingen visade att det valda multimodala transformatornätverket, LayoutLMv2, fungerade bra på Docbank-datasetet. Den skapade datasetet begränsades av klassobalans även om bra prestanda för de större klasserna erhölls. Annotatorverktyget och dess autotaggningsfunktion fungerade bra och den föreslagna pipelinen visade sig vara mycket lovande för att skapa dataset med VVisuallt Rika Dokument.svis besvarar detta examensarbete forskningsfrågorna och föreslår två huvudsakliga möjligheter. Den första är att uppmuntra andra att bygga datauppsättningar med Visuallt Rika Dokument med en liknande pipeline som den som presenteras i denna uppsats. Det andra är att utvärdera möjligheten att skapa den visuella tokeninformationen för LayoutLMv2 som en del av transformatornätverket snarare än att använda en separat CNN.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från K...arrow_drop_down
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    Publikationer från KTH
    Bachelor thesis . 2022
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    Kindbom, Hannes;

    The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The models’ performance were not significantly improved after optimization and it was also dependent on which corpus the models were trained on. An investigation of how a chatbot would affect Hedvig’s adoption rate was also conducted, mainly by reviewing previous studies about chatbots’ effects on user experience. The potential effects on the innovation’s five attributes, relative advantage, compatibility, complexity, trialability and observability were analyzed to answer the problem statement. The results showed that the adoption rate of Hedvig could be positively affected, by improving the first two attributes. The effects a chatbot would have on complexity, trialability and observability were however suggested to be negligible, if not negative. Det vetenskapliga området språkteknologi har fått ökad uppmärksamhet den senaste tiden, men mindre fokus riktas på att jämföra modeller som skiljer sig i komplexitet. Den här kandidatuppsatsen jämför Random Forest med LSTM, genom att undersöka hur väl modellerna kan användas för att klassificera ett meddelande som fråga eller icke-fråga. Jämförelsen gjordes genom att träna och optimera modellerna på historisk chattdata från det svenska försäkringsbolaget Hedvig. Olika typer av word embedding, så som Word2vec och Bag of Words, testades också. Resultaten visade att LSTM uppnådde något högre F1 och accuracy än Random Forest. Modellernas prestanda förbättrades inte signifikant efter optimering och resultatet var också beroende av vilket korpus modellerna tränades på. En undersökning av hur en chattbot skulle påverka Hedvigs adoption rate genomfördes också, huvudsakligen genom att granska tidigare studier om chattbotars effekt på användarupplevelsen. De potentiella effekterna på en innovations fem attribut, relativ fördel, kompatibilitet, komplexitet, prövbarhet and observerbarhet analyserades för att kunna svara på frågeställningen. Resultaten visade att Hedvigs adoption rate kan påverkas positivt, genom att förbättra de två första attributen. Effekterna en chattbot skulle ha på komplexitet, prövbarhet och observerbarhet ansågs dock vara försumbar, om inte negativ.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från K...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    QC 20160318

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från K...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Daniel Svensson; Sverker Sörlin; Katarina Saltzman;

    Can walking trails be understood not only as routes to history and heritage, but also as heritage in and of themselves? The paper explores the articulation of trails as a distinct landscape and mobility heritage, bridging the nature-culture divide and building on physical and intellectual movements over time. The authors aim to contribute to a better understanding of the geography of trails and trailscapes by analysing the emergence of the Swedish-Norwegian trail Finnskogleden. The trail is situated in the border region spanning the former county of Hedmark in present-day Innlandet County, south-eastern Norway, and Värmland County in mid-western Sweden, a forested area where Finnish-speaking immigrants settled from the 16th century to the early 20th century. Archives, literature, interviews, and field visits were used to analyse the emergence and governance of the trail. The main finding is the importance of continuous articulation work by local and regional stakeholders, through texts, maps, maintenance, and mobility. In conclusion, the Finn forest trailscape and its mobility heritage can be seen as an articulation of territory over time, a multilayered process drawing on various environing technologies, making the trail a transformative part of a trans-border political geography. Rörelsearvet: stigar och leder i hållbar och inkluderande kulturarvsförvaltning

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Norsk Geografisk Tid...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Bubla, Boris;

    The recent development of massive multilingual transformer networks has resulted in drastic improvements in model performance. These models, however, are so large they suffer from large inference latency and consume vast computing resources. Such features hinder widespread adoption of the models in industry and some academic settings. Thus there is growing research into reducing their parameter count and increasing their inference speed, with significant interest in the use of knowledge distillation techniques. This thesis uses the existing approach of deep self-attention distillation to develop a task-agnostic distillation of the language agnostic BERT sentence embedding model. It also explores the use of the Switch Transformer architecture in distillation contexts. The result is DistilLaBSE, a task-agnostic distillation of LaBSE used to create a 10 times faster version of LaBSE, whilst retaining over 99% cosine similarity of its sentence embeddings on a holdout test from the same domain as the training samples, namely the OpenSubtitles dataset. It is also shown that DistilLaBSE achieves similar scores when embedding data from two other domains, namely English tweets and customer support banking data. This faster version of LaBSE allows industry practitioners and resourcelimited academic groups to apply a more convenient version of LaBSE to their various applications and research tasks. Den senaste utvecklingen av massiva flerspråkiga transformatornätverk har resulterat i drastiska förbättringar av modellprestanda. Dessa modeller är emellertid så stora att de lider av stor inferenslatens och förbrukar stora datorresurser. Sådana funktioner hindrar bred spridning av modeller i branschen och vissa akademiska miljöer. Således växer det forskning om att minska deras parametrar och öka deras inferenshastighet, med stort intresse för användningen av kunskapsdestillationstekniker. Denna avhandling använder det befintliga tillvägagångssättet med djup uppmärksamhetsdestillation för att utveckla en uppgiftsagnostisk destillation av språket agnostisk BERT- innebördmodell. Den utforskar också användningen av Switch Transformerarkitekturen i destillationskontexter. Resultatet är DistilLaBSE, en uppgiftsagnostisk destillation av LaBSE som används för att skapa en 10x snabbare version av LaBSE, samtidigt som man bibehåller mer än 99 % cosinuslikhet i sina meningsinbäddningar på ett uthållstest från samma domän som träningsproverna, nämligen OpenSubtitles dataset. Det visas också att DistilLaBSE uppnår liknande poäng när man bäddar in data från två andra domäner, nämligen engelska tweets och kundsupportbankdata. Denna snabbare version av LaBSE tillåter branschutövare och resursbegränsade akademiska grupper

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Publikationer från K...arrow_drop_down
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    Kästel, Arne Morten; Vestergaard, Christian;

    In customer support, there are often a lot of repeat questions, and questions that does not need novel answers. In a quest to increase the productivity in the question answering task within any business, there is an apparent room for automatic answering to take on some of the workload of customer support functions. We look at clustering corpora of older queries and texts as a method for identifying groups of semantically similar questions and texts that would allow a system to identify new queries that fit a specific cluster to receive a connected, automatic response. The approach compares the performance of K-means and density-based clustering algorithms on three different corpora using document embeddings encoded with BERT. We also discuss the digital transformation process, why companies are unsuccessful in their implementation as well as the possible room for a new more iterative model. I kundtjänst förekommer det ofta upprepningar av frågor samt sådana frågor som inte kräver unika svar. I syfte att öka produktiviteten i kundtjänst funktionens arbete att besvara dessa frågor undersöks metoder för att automatisera en del av arbetet. Vi undersöker olika metoder för klusteranalys, applicerat på existerande korpusar innehållande texter så väl som frågor. Klusteranalysen genomförs i syfte att identifiera dokument som är semantiskt lika, vilket i ett automatiskt system för frågebevarelse skulle kunna användas för att besvara en ny fråga med ett existerande svar. En jämförelse mellan hur K-means och densitetsbaserad metod presterar på tre olika korpusar vars dokumentrepresentationer genererats med BERT genomförs. Vidare diskuteras den digitala transformationsprocessen, varför företag misslyckas avseende implementation samt även möjligheterna för en ny mer iterativ modell.

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    Devesh Sathya Sri Sairam Sirigina; Aditya Goel; Shareq Mohd Nazir;

    The agricultural sector is the main contributor for the warming from non-CO2 gases, especially methane and nitrous oxide. Existing measures to mitigate these emissions can only reduce but not eliminate these emissions. Owing to the diffused nature of these emissions, it is hard to design a single point measure to address the emissions from the agricultural sector. In our work, we present the first-of-a-kind direct air capture-based process to mitigate these diverse emissions. The process is designed based on thermal catalytic route for the methane conversion, which is coupled to a direct air capture unit for CO2 capture. The process was modelled based on steady state assumptions to estimate the energy requirement per tonne of CO2 equivalent mitigated. Energy estimations were later compared for the two methane removal systems with and without CO2 capture unit. The energy demand per tonne CO2-equivalent removed from the system without CO2 capture unit (only CH4 removal) was found to be 16.54 GJ. For the methane removal system with CO2 capture unit (co-removal of CO2 and CH4), the energy demand is 15.42 GJ per tonne-CO2 equivalent. QC 20230120

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    Publikationer från KTH
    Conference object . 2022
    SSRN Electronic Journal
    Article . 2022
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    Sörlin, Sverker;

    Part of book: ISBN 978-1-009-10023-6QC 20221219

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    Publikationer från KTH
    Part of book or chapter of book . 2022
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    https://doi.org/10.1017/978100...
    Part of book or chapter of book . 2022
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    Evholt, David; Larsson, Oscar;

    Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

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    Jonas Sjöbergh; Viggo Kann;

    We present an online API to access a number of Natural Language Processing services developed at KTH. The services work on Swedish text. They include tokenization, part-of-speech tagging, shallow parsing, compound word analysis, word inflection, lemmatization, spelling error detection and correction, grammar checking, and more. The services can be accessed in several ways, including a RESTful interface, direct socket communication, and premade Web forms. The services are open to anyone. The source code is also freely available making it possible to set up another server or run the tools locally. We have also evaluated the performance of several of the services and compared them to other available systems. Both the precision and the recall for the Granska grammar checker are higher than for both Microsoft Word and Google Docs. The evaluation also shows that the recall is greatly improved when combining all the grammar checking services in the API, compared to any one method, and combining services is made easy by the API. QC 20230328

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    https://ecp.ep.liu.se/index.ph...
    Conference object
    License: cc-by
    Data sources: UnpayWall
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    Stahre, Mattias;

    The use of Deep Learning methods for Document Understanding has been embraced by the research community in recent years. A requirement for Deep Learning methods and especially Transformer Networks, is access to large datasets. The objective of this thesis was to evaluate a state-of-the-art model for Document Layout Analysis on a public and custom dataset. Additionally, the objective was to build a pipeline for building a dataset specifically for Visually Rich Documents. The research methodology consisted of a literature study to find the state-of-the-art model for Document Layout Analysis and a relevant dataset used to evaluate the chosen model. The literature study also included research on how existing datasets in the domain were collected and processed. Finally, an evaluation framework was created. The evaluation showed that the chosen multi-modal transformer network, LayoutLMv2, performed well on the Docbank dataset. The custom build dataset was limited by class imbalance, although good performance for the larger classes. The annotator tool and its auto-tagging feature performed well and the proposed pipelined showed great promise for creating datasets with Visually Rich Documents. In conclusion, this thesis project answers the research questions and suggests two main opportunities. The first is to encourage others to build datasets with Visually Rich Documents using a similar pipeline to the one presented in this paper. The second is to evaluate the possibility of creating the visual token information for LayoutLMv2 as part of the transformer network rather than using a separate CNN. Användningen av Deep Learning-metoder för dokumentförståelse har anammats av forskarvärlden de senaste åren. Ett krav för Deep Learning-metoder och speciellt Transformer Networks är tillgång till stora datamängder. Syftet med denna avhandling var att utvärdera en state-of-the-art modell för analys av dokumentlayout på en offentligt tillgängligt dataset. Dessutom var målet att bygga en pipeline för att bygga en dataset specifikt för Visuallt Rika Dokument. Forskningsmetodiken bestod av en litteraturstudie för att hitta modellen för Document Layout Analys och ett relevant dataset som användes för att utvärdera den valda modellen. Litteraturstudien omfattade också forskning om hur befintliga dataset i domänen samlades in och bearbetades. Slutligen skapades en utvärderingsram. Utvärderingen visade att det valda multimodala transformatornätverket, LayoutLMv2, fungerade bra på Docbank-datasetet. Den skapade datasetet begränsades av klassobalans även om bra prestanda för de större klasserna erhölls. Annotatorverktyget och dess autotaggningsfunktion fungerade bra och den föreslagna pipelinen visade sig vara mycket lovande för att skapa dataset med VVisuallt Rika Dokument.svis besvarar detta examensarbete forskningsfrågorna och föreslår två huvudsakliga möjligheter. Den första är att uppmuntra andra att bygga datauppsättningar med Visuallt Rika Dokument med en liknande pipeline som den som presenteras i denna uppsats. Det andra är att utvärdera möjligheten att skapa den visuella tokeninformationen för LayoutLMv2 som en del av transformatornätverket snarare än att använda en separat CNN.

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    Publikationer från KTH
    Bachelor thesis . 2022
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    Kindbom, Hannes;

    The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The models’ performance were not significantly improved after optimization and it was also dependent on which corpus the models were trained on. An investigation of how a chatbot would affect Hedvig’s adoption rate was also conducted, mainly by reviewing previous studies about chatbots’ effects on user experience. The potential effects on the innovation’s five attributes, relative advantage, compatibility, complexity, trialability and observability were analyzed to answer the problem statement. The results showed that the adoption rate of Hedvig could be positively affected, by improving the first two attributes. The effects a chatbot would have on complexity, trialability and observability were however suggested to be negligible, if not negative. Det vetenskapliga området språkteknologi har fått ökad uppmärksamhet den senaste tiden, men mindre fokus riktas på att jämföra modeller som skiljer sig i komplexitet. Den här kandidatuppsatsen jämför Random Forest med LSTM, genom att undersöka hur väl modellerna kan användas för att klassificera ett meddelande som fråga eller icke-fråga. Jämförelsen gjordes genom att träna och optimera modellerna på historisk chattdata från det svenska försäkringsbolaget Hedvig. Olika typer av word embedding, så som Word2vec och Bag of Words, testades också. Resultaten visade att LSTM uppnådde något högre F1 och accuracy än Random Forest. Modellernas prestanda förbättrades inte signifikant efter optimering och resultatet var också beroende av vilket korpus modellerna tränades på. En undersökning av hur en chattbot skulle påverka Hedvigs adoption rate genomfördes också, huvudsakligen genom att granska tidigare studier om chattbotars effekt på användarupplevelsen. De potentiella effekterna på en innovations fem attribut, relativ fördel, kompatibilitet, komplexitet, prövbarhet and observerbarhet analyserades för att kunna svara på frågeställningen. Resultaten visade att Hedvigs adoption rate kan påverkas positivt, genom att förbättra de två första attributen. Effekterna en chattbot skulle ha på komplexitet, prövbarhet och observerbarhet ansågs dock vara försumbar, om inte negativ.

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    QC 20160318

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    Daniel Svensson; Sverker Sörlin; Katarina Saltzman;

    Can walking trails be understood not only as routes to history and heritage, but also as heritage in and of themselves? The paper explores the articulation of trails as a distinct landscape and mobility heritage, bridging the nature-culture divide and building on physical and intellectual movements over time. The authors aim to contribute to a better understanding of the geography of trails and trailscapes by analysing the emergence of the Swedish-Norwegian trail Finnskogleden. The trail is situated in the border region spanning the former county of Hedmark in present-day Innlandet County, south-eastern Norway, and Värmland County in mid-western Sweden, a forested area where Finnish-speaking immigrants settled from the 16th century to the early 20th century. Archives, literature, interviews, and field visits were used to analyse the emergence and governance of the trail. The main finding is the importance of continuous articulation work by local and regional stakeholders, through texts, maps, maintenance, and mobility. In conclusion, the Finn forest trailscape and its mobility heritage can be seen as an articulation of territory over time, a multilayered process drawing on various environing technologies, making the trail a transformative part of a trans-border political geography. Rörelsearvet: stigar och leder i hållbar och inkluderande kulturarvsförvaltning

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    Bubla, Boris;

    The recent development of massive multilingual transformer networks has resulted in drastic improvements in model performance. These models, however, are so large they suffer from large inference latency and consume vast computing resources. Such features hinder widespread adoption of the models in industry and some academic settings. Thus there is growing research into reducing their parameter count and increasing their inference speed, with significant interest in the use of knowledge distillation techniques. This thesis uses the existing approach of deep self-attention distillation to develop a task-agnostic distillation of the language agnostic BERT sentence embedding model. It also explores the use of the Switch Transformer architecture in distillation contexts. The result is DistilLaBSE, a task-agnostic distillation of LaBSE used to create a 10 times faster version of LaBSE, whilst retaining over 99% cosine similarity of its sentence embeddings on a holdout test from the same domain as the training samples, namely the OpenSubtitles dataset. It is also shown that DistilLaBSE achieves similar scores when embedding data from two other domains, namely English tweets and customer support banking data. This faster version of LaBSE allows industry practitioners and resourcelimited academic groups to apply a more convenient version of LaBSE to their various applications and research tasks. Den senaste utvecklingen av massiva flerspråkiga transformatornätverk har resulterat i drastiska förbättringar av modellprestanda. Dessa modeller är emellertid så stora att de lider av stor inferenslatens och förbrukar stora datorresurser. Sådana funktioner hindrar bred spridning av modeller i branschen och vissa akademiska miljöer. Således växer det forskning om att minska deras parametrar och öka deras inferenshastighet, med stort intresse för användningen av kunskapsdestillationstekniker. Denna avhandling använder det befintliga tillvägagångssättet med djup uppmärksamhetsdestillation för att utveckla en uppgiftsagnostisk destillation av språket agnostisk BERT- innebördmodell. Den utforskar också användningen av Switch Transformerarkitekturen i destillationskontexter. Resultatet är DistilLaBSE, en uppgiftsagnostisk destillation av LaBSE som används för att skapa en 10x snabbare version av LaBSE, samtidigt som man bibehåller mer än 99 % cosinuslikhet i sina meningsinbäddningar på ett uthållstest från samma domän som träningsproverna, nämligen OpenSubtitles dataset. Det visas också att DistilLaBSE uppnår liknande poäng när man bäddar in data från två andra domäner, nämligen engelska tweets och kundsupportbankdata. Denna snabbare version av LaBSE tillåter branschutövare och resursbegränsade akademiska grupper

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    Kästel, Arne Morten; Vestergaard, Christian;

    In customer support, there are often a lot of repeat questions, and questions that does not need novel answers. In a quest to increase the productivity in the question answering task within any business, there is an apparent room for automatic answering to take on some of the workload of customer support functions. We look at clustering corpora of older queries and texts as a method for identifying groups of semantically similar questions and texts that would allow a system to identify new queries that fit a specific cluster to receive a connected, automatic response. The approach compares the performance of K-means and density-based clustering algorithms on three different corpora using document embeddings encoded with BERT. We also discuss the digital transformation process, why companies are unsuccessful in their implementation as well as the possible room for a new more iterative model. I kundtjänst förekommer det ofta upprepningar av frågor samt sådana frågor som inte kräver unika svar. I syfte att öka produktiviteten i kundtjänst funktionens arbete att besvara dessa frågor undersöks metoder för att automatisera en del av arbetet. Vi undersöker olika metoder för klusteranalys, applicerat på existerande korpusar innehållande texter så väl som frågor. Klusteranalysen genomförs i syfte att identifiera dokument som är semantiskt lika, vilket i ett automatiskt system för frågebevarelse skulle kunna användas för att besvara en ny fråga med ett existerande svar. En jämförelse mellan hur K-means och densitetsbaserad metod presterar på tre olika korpusar vars dokumentrepresentationer genererats med BERT genomförs. Vidare diskuteras den digitala transformationsprocessen, varför företag misslyckas avseende implementation samt även möjligheterna för en ny mer iterativ modell.

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    Devesh Sathya Sri Sairam Sirigina; Aditya Goel; Shareq Mohd Nazir;

    The agricultural sector is the main contributor for the warming from non-CO2 gases, especially methane and nitrous oxide. Existing measures to mitigate these emissions can only reduce but not eliminate these emissions. Owing to the diffused nature of these emissions, it is hard to design a single point measure to address the emissions from the agricultural sector. In our work, we present the first-of-a-kind direct air capture-based process to mitigate these diverse emissions. The process is designed based on thermal catalytic route for the methane conversion, which is coupled to a direct air capture unit for CO2 capture. The process was modelled based on steady state assumptions to estimate the energy requirement per tonne of CO2 equivalent mitigated. Energy estimations were later compared for the two methane removal systems with and without CO2 capture unit. The energy demand per tonne CO2-equivalent removed from the system without CO2 capture unit (only CH4 removal) was found to be 16.54 GJ. For the methane removal system with CO2 capture unit (co-removal of CO2 and CH4), the energy demand is 15.42 GJ per tonne-CO2 equivalent. QC 20230120

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    Publikationer från KTH
    Conference object . 2022
    SSRN Electronic Journal
    Article . 2022
    Data sources: Crossref
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