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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.13841">arXiv:2104.13841</a> <span> [<a href="https://arxiv.org/pdf/2104.13841">pdf</a>, <a href="https://arxiv.org/format/2104.13841">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Evaluating Document Representations for Content-based Legal Literature Recommendations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ostendorff%2C+M">Malte Ostendorff</a>, <a href="/search/cs?searchtype=author&query=Ash%2C+E">Elliott Ash</a>, <a href="/search/cs?searchtype=author&query=Ruas%2C+T">Terry Ruas</a>, <a href="/search/cs?searchtype=author&query=Gipp%2C+B">Bela Gipp</a>, <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Julian Moreno-Schneider</a>, <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.13841v1-abstract-short" style="display: inline;"> Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflect the latest advances in recommender systems and representation learning research. Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datase… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.13841v1-abstract-full').style.display = 'inline'; document.getElementById('2104.13841v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.13841v1-abstract-full" style="display: none;"> Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflect the latest advances in recommender systems and representation learning research. Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets. Thus, these studies have limited reproducibility. To address the gap between research and practice, we explore a set of state-of-the-art document representation methods for the task of retrieving semantically related US case law. We evaluate text-based (e.g., fastText, Transformers), citation-based (e.g., DeepWalk, Poincar茅), and hybrid methods. We compare in total 27 methods using two silver standards with annotations for 2,964 documents. The silver standards are newly created from Open Case Book and Wikisource and can be reused under an open license facilitating reproducibility. Our experiments show that document representations from averaged fastText word vectors (trained on legal corpora) yield the best results, closely followed by Poincar茅 citation embeddings. Combining fastText and Poincar茅 in a hybrid manner further improves the overall result. Besides the overall performance, we analyze the methods depending on document length, citation count, and the coverage of their recommendations. We make our source code, models, and datasets publicly available at https://github.com/malteos/legal-document-similarity/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.13841v1-abstract-full').style.display = 'none'; document.getElementById('2104.13841v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication at ICAIL 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.14130">arXiv:2004.14130</a> <span> [<a href="https://arxiv.org/pdf/2004.14130">pdf</a>, <a href="https://arxiv.org/format/2004.14130">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> A Workflow Manager for Complex NLP and Content Curation Pipelines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Juli谩n Moreno-Schneider</a>, <a href="/search/cs?searchtype=author&query=Bourgonje%2C+P">Peter Bourgonje</a>, <a href="/search/cs?searchtype=author&query=Kintzel%2C+F">Florian Kintzel</a>, <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.14130v1-abstract-short" style="display: inline;"> We present a workflow manager for the flexible creation and customisation of NLP processing pipelines. The workflow manager addresses challenges in interoperability across various different NLP tasks and hardware-based resource usage. Based on the four key principles of generality, flexibility, scalability and efficiency, we present the first version of the workflow manager by providing details on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.14130v1-abstract-full').style.display = 'inline'; document.getElementById('2004.14130v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.14130v1-abstract-full" style="display: none;"> We present a workflow manager for the flexible creation and customisation of NLP processing pipelines. The workflow manager addresses challenges in interoperability across various different NLP tasks and hardware-based resource usage. Based on the four key principles of generality, flexibility, scalability and efficiency, we present the first version of the workflow manager by providing details on its custom definition language, explaining the communication components and the general system architecture and setup. We currently implement the system, which is grounded and motivated by real-world industry use cases in several innovation and transfer projects. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.14130v1-abstract-full').style.display = 'none'; document.getElementById('2004.14130v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of the 1st International Workshop on Language Technology Platforms (IWLTP 2020). To appear</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.12190">arXiv:2004.12190</a> <span> [<a href="https://arxiv.org/pdf/2004.12190">pdf</a>, <a href="https://arxiv.org/format/2004.12190">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Towards Discourse Parsing-inspired Semantic Storytelling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a>, <a href="/search/cs?searchtype=author&query=Zaczynska%2C+K">Karolina Zaczynska</a>, <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Juli谩n Moreno-Schneider</a>, <a href="/search/cs?searchtype=author&query=Ostendorff%2C+M">Malte Ostendorff</a>, <a href="/search/cs?searchtype=author&query=Bourgonje%2C+P">Peter Bourgonje</a>, <a href="/search/cs?searchtype=author&query=Berger%2C+M">Maria Berger</a>, <a href="/search/cs?searchtype=author&query=Rauenbusch%2C+J">Jens Rauenbusch</a>, <a href="/search/cs?searchtype=author&query=Schmidt%2C+A">Andr茅 Schmidt</a>, <a href="/search/cs?searchtype=author&query=Wild%2C+M">Mikka Wild</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.12190v1-abstract-short" style="display: inline;"> Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.12190v1-abstract-full').style.display = 'inline'; document.getElementById('2004.12190v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.12190v1-abstract-full" style="display: none;"> Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term goal is the development of an approach for Semantic Storytelling that has broad coverage and that is, furthermore, robust. We provide first results on experiments that involve discourse parsing, applied to a concrete use case, "Explore the Neighbourhood!", which is based on a semi-automatically collected data set with documents about noteworthy people in one of Berlin's districts. Though automatically obtaining annotations for coherence relations from plain text is a non-trivial challenge, our preliminary results are promising. We envision our approach to be combined with additional features (NER, coreference resolution, knowledge graphs <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.12190v1-abstract-full').style.display = 'none'; document.getElementById('2004.12190v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of QURATOR 2020: The conference for intelligent content solutions, Berlin, Germany, February 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.08355">arXiv:2004.08355</a> <span> [<a href="https://arxiv.org/pdf/2004.08355">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a>, <a href="/search/cs?searchtype=author&query=Galanis%2C+D">Dimitrios Galanis</a>, <a href="/search/cs?searchtype=author&query=Labropoulou%2C+P">Penny Labropoulou</a>, <a href="/search/cs?searchtype=author&query=Piperidis%2C+S">Stelios Piperidis</a>, <a href="/search/cs?searchtype=author&query=Wel%C3%9F%2C+M">Martin Wel脽</a>, <a href="/search/cs?searchtype=author&query=Usbeck%2C+R">Ricardo Usbeck</a>, <a href="/search/cs?searchtype=author&query=K%C3%B6hler%2C+J">Joachim K枚hler</a>, <a href="/search/cs?searchtype=author&query=Deligiannis%2C+M">Miltos Deligiannis</a>, <a href="/search/cs?searchtype=author&query=Gkirtzou%2C+K">Katerina Gkirtzou</a>, <a href="/search/cs?searchtype=author&query=Fischer%2C+J">Johannes Fischer</a>, <a href="/search/cs?searchtype=author&query=Chiarcos%2C+C">Christian Chiarcos</a>, <a href="/search/cs?searchtype=author&query=Feldhus%2C+N">Nils Feldhus</a>, <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Juli谩n Moreno-Schneider</a>, <a href="/search/cs?searchtype=author&query=Kintzel%2C+F">Florian Kintzel</a>, <a href="/search/cs?searchtype=author&query=Montiel%2C+E">Elena Montiel</a>, <a href="/search/cs?searchtype=author&query=Doncel%2C+V+R">V铆ctor Rodr铆guez Doncel</a>, <a href="/search/cs?searchtype=author&query=McCrae%2C+J+P">John P. McCrae</a>, <a href="/search/cs?searchtype=author&query=Laqua%2C+D">David Laqua</a>, <a href="/search/cs?searchtype=author&query=Theile%2C+I+P">Irina Patricia Theile</a>, <a href="/search/cs?searchtype=author&query=Dittmar%2C+C">Christian Dittmar</a>, <a href="/search/cs?searchtype=author&query=Bontcheva%2C+K">Kalina Bontcheva</a>, <a href="/search/cs?searchtype=author&query=Roberts%2C+I">Ian Roberts</a>, <a href="/search/cs?searchtype=author&query=Vasiljevs%2C+A">Andrejs Vasiljevs</a>, <a href="/search/cs?searchtype=author&query=Lagzdi%C5%86%C5%A1%2C+A">Andis Lagzdi艈拧</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.08355v1-abstract-short" style="display: inline;"> With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.08355v1-abstract-full').style.display = 'inline'; document.getElementById('2004.08355v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.08355v1-abstract-full" style="display: none;"> With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.08355v1-abstract-full').style.display = 'none'; document.getElementById('2004.08355v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of the 1st International Workshop on Language Technology Platforms (IWLTP 2020). To appear</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.13027">arXiv:2003.13027</a> <span> [<a href="https://arxiv.org/pdf/2003.13027">pdf</a>, <a href="https://arxiv.org/format/2003.13027">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Aksenov%2C+D">Dmitrii Aksenov</a>, <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Juli谩n Moreno-Schneider</a>, <a href="/search/cs?searchtype=author&query=Bourgonje%2C+P">Peter Bourgonje</a>, <a href="/search/cs?searchtype=author&query=Schwarzenberg%2C+R">Robert Schwarzenberg</a>, <a href="/search/cs?searchtype=author&query=Hennig%2C+L">Leonhard Hennig</a>, <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.13027v1-abstract-short" style="display: inline;"> We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT win… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13027v1-abstract-full').style.display = 'inline'; document.getElementById('2003.13027v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.13027v1-abstract-full" style="display: none;"> We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. We also explore how locality modelling, i.e., the explicit restriction of calculations to the local context, can affect the summarization ability of the Transformer. This is done by introducing 2-dimensional convolutional self-attention into the first layers of the encoder. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset. We additionally train our model on the SwissText dataset to demonstrate usability on German. Both models outperform the baseline in ROUGE scores on two datasets and show its superiority in a manual qualitative analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13027v1-abstract-full').style.display = 'none'; document.getElementById('2003.13027v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020). To appear</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.13016">arXiv:2003.13016</a> <span> [<a href="https://arxiv.org/pdf/2003.13016">pdf</a>, <a href="https://arxiv.org/ps/2003.13016">ps</a>, <a href="https://arxiv.org/format/2003.13016">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> A Dataset of German Legal Documents for Named Entity Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Leitner%2C+E">Elena Leitner</a>, <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a>, <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Juli谩n Moreno-Schneider</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.13016v1-abstract-short" style="display: inline;"> We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European le… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13016v1-abstract-full').style.display = 'inline'; document.getElementById('2003.13016v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.13016v1-abstract-full" style="display: none;"> We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13016v1-abstract-full').style.display = 'none'; document.getElementById('2003.13016v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020). To appear</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.12900">arXiv:2003.12900</a> <span> [<a href="https://arxiv.org/pdf/2003.12900">pdf</a>, <a href="https://arxiv.org/format/2003.12900">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Orchestrating NLP Services for the Legal Domain </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Juli谩n Moreno-Schneider</a>, <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a>, <a href="/search/cs?searchtype=author&query=Montiel-Ponsoda%2C+E">Elena Montiel-Ponsoda</a>, <a href="/search/cs?searchtype=author&query=Rodriguez-Doncel%2C+V">V铆ctor Rodriguez-Doncel</a>, <a href="/search/cs?searchtype=author&query=Revenko%2C+A">Artem Revenko</a>, <a href="/search/cs?searchtype=author&query=Karampatakis%2C+S">Sotirios Karampatakis</a>, <a href="/search/cs?searchtype=author&query=Khvalchik%2C+M">Maria Khvalchik</a>, <a href="/search/cs?searchtype=author&query=Sageder%2C+C">Christian Sageder</a>, <a href="/search/cs?searchtype=author&query=Gracia%2C+J">Jorge Gracia</a>, <a href="/search/cs?searchtype=author&query=Maganza%2C+F">Filippo Maganza</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.12900v1-abstract-short" style="display: inline;"> Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.12900v1-abstract-full').style.display = 'inline'; document.getElementById('2003.12900v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.12900v1-abstract-full" style="display: none;"> Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.12900v1-abstract-full').style.display = 'none'; document.getElementById('2003.12900v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020). To appear</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.08402">arXiv:1909.08402</a> <span> [<a href="https://arxiv.org/pdf/1909.08402">pdf</a>, <a href="https://arxiv.org/format/1909.08402">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Enriching BERT with Knowledge Graph Embeddings for Document Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ostendorff%2C+M">Malte Ostendorff</a>, <a href="/search/cs?searchtype=author&query=Bourgonje%2C+P">Peter Bourgonje</a>, <a href="/search/cs?searchtype=author&query=Berger%2C+M">Maria Berger</a>, <a href="/search/cs?searchtype=author&query=Moreno-Schneider%2C+J">Julian Moreno-Schneider</a>, <a href="/search/cs?searchtype=author&query=Rehm%2C+G">Georg Rehm</a>, <a href="/search/cs?searchtype=author&query=Gipp%2C+B">Bela Gipp</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1909.08402v1-abstract-short" style="display: inline;"> In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classific… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.08402v1-abstract-full').style.display = 'inline'; document.getElementById('1909.08402v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.08402v1-abstract-full" style="display: none;"> In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classification task. For a more coarse-grained classification using eight labels we achieve an F1- score of 87.20, while a detailed classification using 343 labels yields an F1-score of 64.70. We make the source code and trained models of our experiments publicly available <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.08402v1-abstract-full').style.display = 'none'; document.getElementById('1909.08402v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> 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