Semantic Domains in Akkadian Text

The article examines the possibilities offered by language technology for analyzing semantic fields in Akkadian. The corpus of data for our research group is the existing electronic corpora, Open richly annotated cuneiform corpus (ORACC). In addition to more traditional Assyriological methods, the article explores two language technological methods: Pointwise mutual information (PMI) and Word2vec.Peer reviewe

CyberResearch on the Ancient Near East and Eastern Mediterranean

CyberResearch on the Ancient Near East and Neighboring Regions provides case studies on archaeology, objects, cuneiform texts, and online publishing, digital archiving, and preservation. Eleven chapters present a rich array of material, spanning the fifth through the first millennium BCE, from Anatolia, the Levant, Mesopotamia, and Iran. Customized cyber- and general glossaries support readers who lack either a technical background or familiarity with the ancient cultures. Edited by Vanessa Bigot Juloux, Amy Rebecca Gansell, and Alessandro Di Ludovico, this volume is dedicated to broadening the understanding and accessibility of digital humanities tools, methodologies, and results to Ancient Near Eastern Studies. Ultimately, this book provides a model for introducing cyber-studies to the mainstream of humanities research

Data formats for phonological corpora

The goal of the present chapter is to explore the possibility of providing the research (but also the industrial) community that commonly uses spoken corpora with a stable portfolio of well-documented standardised formats that allow a high re-use rate of annotated spoken resources and, as a consequence, better interoperability across tools used to produce or exploit such resources.Comment: Handbook of Corpus Phonology Oxford University Press (Ed.) (2012

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given