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- Leroi, Armand M., et al.
(författare)
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On Revolutions
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Sometimes the normal course of events is disrupted by a particularly swift and profound change. Historians have often referred to such changes as "revolutions" and, though they have identied many of them, they have rarely supported their claims with statistical evidence. Here we present a method to identify revolutions based on a measure of the multivariate rate of change called Foote Novelty. We dene revolutions as those periods of time when the value of this measure, F, can, by a non-parametric test, be shown to be signicantly greater than the background rate. Our method also identies conservative periods when the rate of change is unusually low. Importantly, our method permits searching for revolutions over any time scale that the data permit. We apply it to several quantitative data sets that capture long-term political, social and cultural changes and, in some of them, identify revolutions, both well known and not. Our method is a general one that can be applied to any phenomenon captured by multivariate time series data of sufficient quality.
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| 2. |
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| 3. |
- Rehm, Georg, et al.
(författare)
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The strategic impact of META-NET on the regional, national and international level
- 2016
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Ingår i: Language resources and evaluation. - : Springer Science and Business Media LLC. - 1574-020X .- 1572-8412 .- 1574-0218. ; 50:2, s. 351-374
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Tidskriftsartikel (refereegranskat)abstract
- This article provides an overview of the dissemination work carried out in META-NET from 2010 until 2015; we describe its impact on the regional, national and international level, mainly with regard to politics and the funding situation for LT topics. The article documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.
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| 4. |
- Wang, Kanix, et al.
(författare)
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NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding
- 2021
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Ingår i: npj Systems Biology and Applications. - : Springer Science and Business Media LLC. - 2056-7189. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.
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| 5. |
- Xie, Qianqian, et al.
(författare)
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Knowledge-enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization
- 2024
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Ingår i: IEEE journal of biomedical and health informatics. - Piscataway, NJ : IEEE. - 2168-2194 .- 2168-2208. ; 8:4, s. 1836-1847
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Tidskriftsartikel (refereegranskat)abstract
- Given the overwhelming and rapidly increasing volumes of the published biomedical literature, automatic biomedical text summarization has long been a highly important task. Recently, great advances in the performance of biomedical text summarization have been facilitated by pre-trained language models (PLMs) based on fine-tuning. However, existing summarization methods based on PLMs do not capture domain-specific knowledge. This can result in generated summaries with low coherence, including redundant sentences, or excluding important domain knowledge conveyed in the full-text document. Furthermore, the black-box nature of the transformers means that they lack explainability, i.e. it is not clear to users how and why the summary was generated. The domain-specific knowledge and explainability are crucial for the accuracy and transparency of biomedical text summarization methods. In this article, we aim to address these issues by proposing a novel domain knowledge-enhanced graph topic transformer (DORIS) for explainable biomedical text summarization. The model integrates the graph neural topic model and the domain-specific knowledge from the Unified Medical Language System (UMLS) into the transformer-based PLM, to improve the explainability and accuracy. Experimental results on four biomedical literature datasets show that our model outperforms existing state-of-the-art (SOTA) PLM-based summarization methods on biomedical extractive summarization. Furthermore, our use of graph neural topic modeling means that our model possesses the desirable property of being explainable, i.e. it is straightforward for users to understand how and why the model selects particular sentences for inclusion in the summary. The domain-specific knowledge helps our model to learn more coherent topics, to better explain the performance. © IEEE
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