2,067 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Location Reference Recognition from Texts: A Survey and Comparison
A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Archaeological palaeoenvironmental archives: challenges and potential
This Arts and Humanities Research Council (AHRC) sponsored collaborative doctoral project represents one of
the most significant efforts to collate quantitative and qualitative data that can elucidate practices related to
archaeological palaeoenvironmental archiving in England. The research has revealed that archived
palaeoenvironmental remains are valuable resources for archaeological research and can clarify subjects that
include the adoption and importation of exotic species, plant and insect invasion, human health and diet, and
plant and animal husbandry practices. In addition to scientific research, archived palaeoenvironmental remains
can provide evidence-based narratives of human resilience and climate change and offer evidence of the
scientific process, making them ideal resources for public science engagement. These areas of potential have
been realised at an imperative time; given that waterlogged palaeoenvironmental remains at significant sites
such as Star Carr, Must Farm, and Flag Fen, archaeological deposits in towns and cities are at risk of decay due
to climate change-related factors, and unsustainable agricultural practices. Innovative approaches to collecting
and archiving palaeoenvironmental remains and maintaining existing archives will permit the creation of an
accessible and thorough national resource that can service archaeologists and researchers in the related fields
of biology and natural history. Furthermore, a concerted effort to recognise absences in archaeological
archives, matched by an effort to supply these deficiencies, can produce a resource that can contribute to an
enduring geographical and temporal record of England's biodiversity, which can be used in perpetuity in the
face of diminishing archaeological and contemporary natural resources.
To realise these opportunities, particular challenges must be overcome. The most prominent of these include
inconsistent collection policies resulting from pressures associated with shortages in storage capacity and
declining specialist knowledge in museums and repositories combined with variable curation practices. Many of
these challenges can be resolved by developing a dedicated storage facility that can focus on the ongoing
conservation and curation of palaeoenvironmental remains. Combined with an OASIS + module designed to
handle and disseminate data pertaining to palaeoenvironmental archives, remains would be findable,
accessible, and interoperable with biological archives and collections worldwide. Providing a national centre for
curating palaeoenvironmental remains and a dedicated digital repository will require significant funding.
Funding sources could be identified through collaboration with other disciplines. If sufficient funding cannot be
identified, options that would require less financial investment, such as high-level archive audits and the
production of guidance documents, will be able to assist all stakeholders with the improved curation,
management, and promotion of the archived resource
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
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