Search

The acquisition of knowledge is essential for specialized translation, and the representation of specialized phraseology in terminological knowledge bases facilitates this process. The aim of this study is two-fold. Firstly, it describes how the semantic annotation of the predicate-argument structure of sentences mentioning named rivers can be addressed from the perspective of Frame-based Terminology. The results show that this approach, including the semantic variables of verb lexical domain, semantic role, and semantic category, provides valuable insights into the knowledge structures underlying the usage of named rivers in specialized texts. Secondly, this study explores whether the bracketing of a three-component multiword term can be predicted from the semantic information encoded in the sentence where the ternary compound and a named river are used as arguments. The semantic variables of lexical domain, semantic role, and semantic category allowed us to construct two machine-learning models capable of accurately predicting ternary-compound bracketing. [ABSTRACT FROM AUTHOR]

This paper stems from the project A World of Possibilities. Modal pathways over an extra-long period of time: the diachrony of modality in the Latin language (WoPoss) which involves a corpus-based approach to the study of modality in the history of the Latin language. Linguistic annotation and, in particular, the semantic annotation of modality is a keystone of the project. Besides the difficulties intrinsic to any annotation task dealing with semantics, our annotation scheme involves multiple layers of annotation that are interconnected, adding complexity to the task. Considering the intricacies of our fine-grained semantic annotation, we needed to develop well-documented schemas in order to control the consistency of the annotation, but also to enable an efficient reuse of our annotated corpus. This paper presents the different elements involved in the annotation task, and how the description and the relations between the different linguistic components were formalised and documented, combining schema languages with XML documentation. [ABSTRACT FROM AUTHOR]

Semantic search encompasses advanced technological approaches to information discovery and retrieval, employing semantic techniques to extract information from intricately structured data sources. An effective search engine must have the ability of accessing and retrieving information of interest by employing reasoning with conceptual models. However, the structural and semantic information intrinsic in conceptual models is not readily amenable to reasoning and AI-enhanced semantic processing. Therefore, the chosen model should enable understanding the meaning of concepts and the relationships between them. Il should also carefully consider the context of the search, ultimately enhancing the accuracy of the returned results. Among the models that fulfill these objectives, conceptual graphs stand out as particularly interesting. They are built upon a robust theoretical framework that spans multiple domains, including philosophical, psychological, linguistic, and artificial intelligence disciplines. In this paper, we describe a method for semantic search driven by conceptual graph-based representation, and a powerful matching reasoning supported by a projection operation between the semantic annotations associated with the document and the submitted query. [ABSTRACT FROM AUTHOR]

Understanding citations within their context is a complex task in information science, critical for bibliometric analysis. The study of citation contexts and their types has been a central issue in recent work on citations. In this paper, we present an experiment on the semantic annotation of citation contexts using a rule-based approach. We processed articles from seven PLOS journals and performed semantic annotation of citation contexts based on linguistic resources we constructed. We built on previous work on verb form analysis, n-grams, and semantic category modeling in the form of a linguistic ontology. Based on our observations, we propose directions of work for the constitution of a semantically annotated corpora. The intermediate results obtained lead us to formulate hypotheses on the relation between the IMRaD structure and certain semantic categories. Furthermore, our results demonstrate the semantic richness of citation contexts and underscore the importance of access to full-text articles for ontology population in open science. The findings suggest that semantic categories vary across disciplines and rhetorical structures, necessitating further exploration with larger and more diverse datasets. [ABSTRACT FROM AUTHOR]

Scientific uncertainty is an integral part of the research process and inherent to the construction of new knowledge. In this paper, we investigate the ways in which uncertainty is expressed in articles and propose a new interdisciplinary annotation framework to categorize sentences containing uncertainty expressions along five dimensions. We propose a method for the automatic annotation of sentences based on linguistic patterns for identifying the expressions of scientific uncertainty that have been derived from a corpus study. We processed a corpus of 5956 articles from 22 journals in three different discipline groups, which were annotated using our automatic annotation method. We evaluate our annotation method and study the distribution of uncertainty expressions across the different journals and categories. The results show a predominant concentration of the distribution of the scientific uncertainty expressions in the Results and Discussion section (71.4%), followed by 12.5% of expressions in the Background section, and the largest proportion of uncertainty expressions, approximately 70.3%, are formed as author(s) statements. Our research contributes methodological advances and insights into the diverse manifestations of scientific uncertainty across disciplinary domains and provides a basis for ongoing exploration and refinement of the understanding of scientific uncertainty communication. [ABSTRACT FROM AUTHOR]

Semantic annotation, a pivotal technology facilitating comprehension within textual data, serves as the process of appending supplementary information or metadata to text, thereby augmenting its meaning. Given the inherent ambiguity of natural language, which renders the data susceptible to multiple interpretations, the task of discerning the intended meaning from raw data proves significantly more challenging than interpreting structured text. This ambiguity necessitates mechanisms to render text comprehensible to both machines and humans, thereby enabling the efficient extraction and innovation of various subjects. In response to these challenges, considerable research efforts have been dedicated to advancing the methodologies of text annotation. This review explores the role of semantic annotation in addressing contextual ambiguity, underspecified semantic representations, formal semantics, and the resolution of semantic ambiguities. By integrating additional data into the text, semantic annotation establishes a synergy with the Linked Open Data (LOD) framework, thereby providing context and enhancing machine readability. LOD, a practice of publishing structured data on the web to facilitate interlinking and utility, benefits from semantic annotation as it improves data publishing, linking, and enrichment processes. This enhancement directly contributes to the precision of web search results. The literature on semantic annotation, encompassing tools, methods, and techniques, as well as its relationship with LOD, is meticulously reviewed. This paper employs a systematic approach to select pertinent articles, highlighting state-of-the-art methods in semantic annotation, including deep learning and ontology-based techniques. The exploration aims to delineate the evolution of semantic annotation practices and their consequential impact on the LOD ecosystem, underscoring the mutual enrichment of both fields. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of leveraging free-text descriptions in Electronic Health Records (EHR) for clinical research and healthcare improvement. Despite the potential of this data, its direct interpretation by computers is limited. Semantic annotation emerges as a method to make EHR free text machine interpretable but struggles with specific domain ontologies and faces heightened difficulties in psychiatry. To tackle these challenges, this study proposes a system based on unsupervised learning techniques to extract entities and their relationships, aligning them with a domain ontology. The effectiveness of this system has been validated within PsyCARE project by analyzing 60 patient discharge summaries. [ABSTRACT FROM AUTHOR]

The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for the processing of harmonised datasets and data streams. Our work is based on a structured approach that leverages the principles of linked-data modelling and semantics, as well as a data enrichment toolchain framework developed around NGSI-LD. Within this framework, we reveal the potential for enrichment and linkage techniques to reshape how data are exploited in smart cities, with a particular focus on citizen-centred initiatives. Moreover, we showcase the effectiveness of these data processing techniques through specific examples of entity transformations. The findings, which focus on improving data comprehension and bolstering smart city advancements, set the stage for the future exploration and refinement of the symbiosis between semantic data and smart city ecosystems. [ABSTRACT FROM AUTHOR]

This article evaluates the integration of digital editions, computational text analysis and digital scholarly editing in the context of an introductory undergraduate course on Italian literature and digital humanities taught at a US university. It offers specific examples of employing the apparatus of several digital platforms dedicated to the study of foundational authors in the Italian literary tradition (Dante, Petrarch, Boccaccio and Leopardi), and of gaining familiarity with a suite of digital tools for text analysis and editing, namely Voyant Tools, Recogito, Oxygen, Gephi, Transkribus Lite and OpenRefine. The discussion of digital project interfaces examines the student user experience of different design approaches, while the illustrations of tool exercises explore how these could support the close attention to a text and facilitate the navigation between its micro and macro frameworks of interpretation. The article furthermore suggests that digital text analysis could reinforce student appreciation of the signifying value of textual form and genre, and that the pedagogical method of digital text editing creates opportunities for situated learning. In conclusion, it argues that the academic work of students at the undergraduate level could be harnessed by the scaffolded methods of faculty-led digital research projects and contribute to the creation of public knowledge. [ABSTRACT FROM AUTHOR]

Background Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. Objective To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). Methods The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. Results The most frequent keyword is "clinical trial" (n = 18,777), and the most frequent disease-specific keyword is "breast neoplasms" (n = 1,943). Most data items are available in English (n = 545,749) and German (n = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. Conclusion To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing. [ABSTRACT FROM AUTHOR]

A number of high-frequency word lists have been created to help foreign language learners master English vocabulary. These word lists, despite their widespread use, did not take word meaning into consideration. Foreign language learners are unclear on which meanings they should focus on first. To address this issue, we semantically annotated the Corpus of Contemporary American English (COCA) and the British National Corpus (BNC) with high accuracy using a BERT model. From these annotated corpora, we calculated the semantic frequency of different senses and filtered out 5000 senses to create a High-frequency Sense List. Subsequently, we checked the validity of this list and compared it with established influential word lists. This list exhibits three notable characteristics. First, it achieves stable coverage in different corpora. Second, it identifies high-frequency items with greater accuracy. It achieves comparable coverage with lists like GSL, NGSL, and New-GSL but with significantly fewer items. Especially, it includes everyday words that used to fall off high-frequency lists without requiring manual adjustments. Third, it describes clearly which senses are most frequently used and therefore should be focused on by beginning learners. This study represents a pioneering effort in semantic annotation of large corpora and the creation of a word list based on semantic frequency.

[Purpose/Significance] Aiming at the semantic missing and incomplete problems in the process of image organization and retrieval, a framework for semantic description of images in social media is proposed to enrich the existing theoretical system of image description, improve the efficiency and utilization of image retrieval, and provide a reference for the realization of the automatic semantic annotation of images. [Method/Process] First, we conducted a survey and analysis of research progress related to image description both domestically and internationally, summarizing the existing theories of image description and annotation, metadata specifications, and related technical methods. Second, based on the image metadata standards and the theory of hierarchical and categorical description of image features, we constructed a semantic description framework for social media images, focusing on seven layers: external feature layer, content layer, object layer, relationship layer, scene layer, event layer, and emotional layer. We also elaborated in detail the various semantic layers and their interrelationships. Finally, we verified the feasibility of the image semantic description framework by describing the examples of character images and landscape images. [Results/Conclusions] The results of the descriptive examples of character images and landscape images indicate that the image semantic description framework can eliminate the "semantic gap" in image description through semantic associations between different layers, and achieve a multi-faceted, multi-dimensional, and multi-level structured and semantic description of the external and content features of images. It has strong portability and flexibility. However, there are also certain limitations and areas for improvement in this paper: 1) Based on the image semantic description framework proposed in this paper, a prototype system based on image annotation needs to be developed; 2) The images posted by users on social media are closely related to the situation, and they are more likely to express emotions. In the future, more research on the semantic layer of images can be conducted based on the text information posted by users; 3) Future research can further explore the application of deep learning in image and text fusion to achieve more accurate event and emotion recognition. By constructing a more complex neural network structure, the event and emotion information in the image can be deeply mined and fused; 4) When describing images, the study should pay attention not only to static visual features, but also to consider the dynamic course of events. Future frameworks could attempt to combine static and dynamic information to provide richer, more vivid descriptions of images.

Featured Application: Within the context of the Medical Informatics Initiative (MII) funded by the Federal Ministry of Research and Education (BMBF), German academic medicine constructs a nationally harmonized, joint infrastructure enabling secondary use of patient data from heterogeneous clinical IT sources. The semantic annotation of such data is a prerequisite for cross-site usage. The approach and software described in and published jointly with this manuscript may not only facilitate a more efficient semantic annotation process but also promote fully interoperable semantic representations by catalyzing convergent user decisions in the presence of non-uniqueness of annotation choices. The cross-institutional secondary use of medical data benefits from structured semantic annotation, which ideally enables the matching and merging of semantically related data items from different sources and sites. While numerous medical terminologies and ontologies, as well as some tooling, exist to support such annotation, cross-institutional data usage based on independently annotated datasets is challenging for multiple reasons: the annotation process is resource intensive and requires a combination of medical and technical expertise since it often requires judgment calls to resolve ambiguities resulting from the non-uniqueness of potential mappings to various levels of ontological hierarchies and relational and representational systems. The divergent resolution of such ambiguities can inhibit joint cross-institutional data usage based on semantic annotation since data items with related content from different sites will not be identifiable based on their respective annotations if different choices were made without further steps such as ontological inference, which is still an active area of research. We hypothesize that a collaborative approach to the semantic annotation of medical data can contribute to more resource-efficient and high-quality annotation by utilizing prior annotational choices of others to inform the annotation process, thus both speeding up the annotation itself and fostering a consensus approach to resolving annotational ambiguities by enabling annotators to discover and follow pre-existing annotational choices. Therefore, we performed a requirements analysis for such a collaborative approach, defined an annotation workflow based on the requirement analysis results, and implemented this workflow in a prototypical Collaborative Annotation Tool (CoAT). We then evaluated its usability and present first inter-institutional experiences with this novel approach to promote practically relevant interoperability driven by use of standardized ontologies. In both single-site usability evaluation and the first inter-institutional application, the CoAT showed potential to improve both annotation efficiency and quality by seamlessly integrating collaboratively generated annotation information into the annotation workflow, warranting further development and evaluation of the proposed innovative approach. [ABSTRACT FROM AUTHOR]

Alongside construction reports and descriptions, account books and the results of archaeological and architectural history investigations, inventories are one of the most important sources for the spatial structures of medieval castles. Especially room-related inventories structured by room names provide an insight into the status quo of the spatial structures. The room names and descriptions of the rooms, together with the objects stored in them at the moment of the creation of the inventory, provide an insight into the function, use and design of the rooms in these buildings, many of which are now only in ruins or in a heavily overbuilt state. In addition to the physical elements, inventories also provide an essential basis for research into the social space of the castle. Spatial information often reveals, explicitly or implicitly, information about the castle's inhabitants, their actions and spaces as well as memorial practices. Historical spatial analysis, taking into account interdisciplinary spatial concepts and terminologies, opens the view to these aspects and allows a new perspective on the castle building as a living space of a diverse community of people of all genders and different social groups. Using digital methods, serial sources such as inventories can be systematically analysed in order to study a larger number of castles comparatively, for example with regard to spatial aspects. [ABSTRACT FROM AUTHOR]

This paper presents a number of resources for examining the expression of modality in the Gospels. The main resource is an XML-TEI dataset that contains the linguistic annotation of a predefined list of potentially modal markers in both Ancient Greek and Latin. When one of these markers conveys a modal meaning, each constituent of the modal passage (i.e., the marker, its scope, and the modal relation between them) is annotated with a great level of detail through several linguistic features. One of the original features of our dataset is the implementation of a cross-referencing system that enables the alignment of the potentially modal markers of both languages. To facilitate the exploitation of our data by those unfamiliar with XML technologies, we also provide summary tables with the most relevant features of the annotation. In addition, a program written in Apache Ant allows any user to generate the summary sheets and to align modal passages in both Ancient Greek and Latin with any other language available in the Multilingual Bible Parallel Corpus. This contribution presents the details of the semantic annotation and its formalization, and how our resources may be exploited within semantics and translation studies. In addition, the encoding strategies implemented are relevant for other projects dealing with the combination of multiple layers of (linguistic) annotation and/or tackling the development of parallel corpora. Explore modality in the Ancient Greek and Latin Gospels and compare their translations across nearly 100 languages through a user-friendly XML-TEI dataset. [ABSTRACT FROM AUTHOR]

In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware's operational efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a 3D model based on semantic annotation is constructed with the goal of improving the efficiency of building interior design. By analyzing the basic methods of semantic annotation for building interiors, the method of positioning and map construction is selected to obtain the indoor point cloud data. The distance between 3D spatial lines is calculated using the frame line extraction algorithm, and the target area of the frame line candidate is divided according to the distance. According to the principle of detecting raster circles using the Hough transform, an interior design structure recognition method is proposed for recognizing windows, doors, and walls in building interiors. The results show that the modeling time of the semantically annotated 3D model is 10 seconds faster than the other models on the wall; 9 seconds are saved on the door modeling, and 7 seconds are saved on the window modeling. The visualization effect of semantically annotated 3D models is mostly concentrated in (0.5-1), and a large number of data points are distributed in (0.6-0.9), which indicates that the visualization effect of semantically annotated 3D models is better. The semantically annotated 3D model proposed in this paper can improve the visualization of architectural interior design, which can improve the efficiency of designers to a certain extent.

• Use of CNN to segment 3D reality capture 3D point clouds of built heritage. • Use ML for identification of the period of monuments' building parts. • Trained CNN on Cypriot built heritage. • Dataset of segmented and annotated built heritage examples. • Open, online accessible tool for education. The article presents a workflow based on Deep Neural Networks (DNNs) and Support Vector Machine (SVM) for identifying architectural stylistic influences of segmented building parts of Cypriot historical architecture in 3D. The research contributes in the field of Digital Cultural Heritage (DCH) by applying Machine Learning (ML) and Deep Learning (DL) on recently published DCH data [1] , with the aim to accelerate the segmentation and annotation process of Historic Building Information modelling (HBIM) that is currently based on time-consuming manual processes. The method presented works on reality captured data by 3D documentation techniques, precisely, Terrestrial Laser Scanning (TLS) or Photogrammetry. This workflow was developed to enable the operation of an online platform, 1 1 https://annfass-srv.cs.ucy.ac.cy. which also provides access to the building data presented here. Ultimately, the results of the presented method are accessible to scholars and students via this platform which provides multiple functionalities for researchers in the field to use. [ABSTRACT FROM AUTHOR]

In an increasingly multilingual digital world, information management tools must support the simultaneous use and matching of multiple natural languages. A prerequisite for this is that the underlying database engine seamlessly processes multilingual data across languages. However, most natural language processing-based techniques have focused on developing monolingual matching algorithms, often ignoring context knowledge and external domain-based sources, which lead to incomplete and inaccurate matching results in a multilingual environment. The purpose of this study is to propose an adaptive semantic matching method with context knowledge and user involvement as two new dimensions for matching the semantically related entities ontologies. We present a comprehensive evaluation of our solution by applying it in a multilingual e-commerce platform case study, which performed well on matching accuracy. [ABSTRACT FROM AUTHOR]

Annotations enrich text corpora and provide necessary labels for natural language processing studies. To reason and infer underlying implicit knowledge captured by labels, an ontology is needed to provide a semantically annotated corpus with structured domain knowledge. Utilizing a corpus of adverse event documents annotated for sepsis-related signs and symptoms as a use case, this paper details how a terminology and corresponding ontology were developed. The Annotated Adverse Event NOte TErminology (AAENOTE) represents annotated documents and assists annotators in annotating text. In contrast, the complementary Catheter Infection Indications Ontology (CIIO) is intended for clinician use and captures domain knowledge needed to reason and infer implicit information from data. The approach taken makes ontology development understandable and accessible to domain experts without formal ontology training. [ABSTRACT FROM AUTHOR]

Proliferation of data sources associated to Internet of Things (IoT) deployment as well as those bound to Open Data Portals (e.g. European Data Portal, Municipalities Open Data Portals, etc.) and Social Media platforms is creating an abundance of information that is called to bring benefits for both the private and public sectors, through the development of added-value services, increasing administrations’ transparency and availability or fostering efficiency of public services. However, pieces of information without a context are significantly less valuable. Raw data lacks semantics and it is highly heterogeneous from one data-source to another. This poses a challenge to make it useful. To turn all this data into valuable information it is necessary to enable its combination so that meaningful context can be created. Moreover, it is fundamental to define the mechanisms enabling the adoption and orchestration of advanced (typically AI-enabled) data processing techniques to be applied over the harmonized datasets and data-streams. This paper presents the Data Enrichment Toolchain (DET) that provides the necessary harmonization and enrichment to datasets and data-streams coming from heterogeneous sources. The value of the enriched data lies on the one hand in the transfer of the data into a semantically grounded knowledge graph and, on the other hand, in the creation of new data through linking, aggregating and reasoning on the data. In both cases, the benefit of employing linked-data modelling and semantics comes from the extension of the metadata that is associated to every piece of information. Furthermore, the experimental evaluation of the DET implementation that we have carried out is also presented in the paper.

Extreme weather events, such as windstorms, hurricanes, and heat waves, exert a significant impact on global natural catastrophes and pose substantial challenges for weather forecasting systems. To enhance the accuracy and preparedness for extreme weather events, this study explores the potential of using expert crowdsourcing in storm forecasting research through the application of stigmergic collaboration. We present the development and implementation of an expert Crowdsourcing for Semantic Annotation of Atmospheric Phenomena (eCSAAP) system, designed to leverage the collective knowledge and experience of meteorological experts. Through a participatory co-creation process, we iteratively developed a web-based annotation tool capable of capturing multi-faceted insights from weather data and generating visualizations for expert crowdsourcing campaigns. In this context, this article investigates the intrinsic coordination among experts engaged in crowdsourcing tasks focused on the semantic annotation of extreme weather events. The study brings insights about the behavior of expert crowds by considering the cognitive biases and highlighting the impact of existing annotations on the quality of data gathered from the crowd and the collective knowledge generated. The insights regarding the crowdsourcing dynamics, particularly stigmergy, offer a promising starting point for utilizing stigmergic collaboration as an effective coordination mechanism for weather experts in crowdsourcing platforms but also in other domains requiring expertise-driven collective intelligence.

The emergence of ChatGPT has sensitized the general public, including the legal profession, to large language models' (LLMs) potential uses (e.g., document drafting, question answering, and summarization). Although recent studies have shown how well the technology performs in diverse semantic annotation tasks focused on legal texts, an influx of newer, more capable (GPT-4) or cost-effective (GPT-3.5-turbo) models requires another analysis. This paper addresses recent developments in the ability of LLMs to semantically annotate legal texts in zero-shot learning settings. Given the transition to mature generative AI systems, we examine the performance of GPT-4 and GPT-3.5-turbo(-16k), comparing it to the previous generation of GPT models, on three legal text annotation tasks involving diverse documents such as adjudicatory opinions, contractual clauses, or statutory provisions. We also compare the models' performance and cost to better understand the trade-offs. We found that the GPT-4 model clearly outperforms the GPT-3.5 models on two of the three tasks. The cost-effective GPT-3.5-turbo matches the performance of the 20× more expensive text-davinci-003 model. While one can annotate multiple data points within a single prompt, the performance degrades as the size of the batch increases. This work provides valuable information relevant for many practical applications (e.g., in contract review) and research projects (e.g., in empirical legal studies). Legal scholars and practicing lawyers alike can leverage these findings to guide their decisions in integrating LLMs in a wide range of workflows involving semantic annotation of legal texts.

In the contribution, we provide a theory-based and corpus-verified description of expressions for measure in Czech. We demonstrate that the measure expressions may modify quantity of entities (approximately ten boys), internal characteristics of events (he works a lot), properties (very big) and relations (completely without sound). We distinguish between the measure expressions that are an answer to the question To what extent? (Extent-modifiers) and expressions that modify an answer to the question How many? (Quantity-modifiers). The Extent-modifiers are formally, structurally and semantically more diverse than the Quantity-modifiers. For the Quantity-modifiers a list of forms and functions is provided. Theoretical knowledge stemming from the analysis will subsequently be used to improve the annotation in the Prague Dependency Treebanks. It can be also useful for other semantically-oriented descriptions of language. [ABSTRACT FROM AUTHOR]

When treating structured health-system-related knowledge, the establishment of an over-dimension to guide the separation of entities becomes essential. This is consistent with the information retrieval processes aimed at defining a coherent and dynamic way - meaning by that the multilevel integration of medical textual inputs and computational interpretation - to replicate the flow of data inserted in the clinical records. This study presents a strategic technique to categorize the clinical entities related to patients affected by neurodegenerative diseases. After a pre-processing range of tasks over paper-based and handwritten medical records, and through subsequent machine learning and, more specifically, natural language processing operations over the digitized clinical records, the research activity provides a semantic support system to detect the main symptoms and locate them in the appropriate clusters. Finally, the supervision of the experts proved to be essential in the correspondence sequence configuration aimed at providing an automatic reading of the clinical records according to the clinical data that is needed to predict the detection of neurodegenerative disease symptoms. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ingeniería - UPTC is the property of Universidad Pedagogica y Tecnologica de Colombia, Facultad de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)