Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Chemistry
Title: Electrical Properties of Nb‐, Ga‐, and Y‐Substituted Nanocrystalline Anatase TiO2 Prepared by Hydrothermal Synthesis
Abstract: Nanocrystalline anatase titanium dioxide powders were produced by a hydrothermal synthesis route in pure form and substituted with trivalent Ga3+ and Y3+ or pentavalent Nb5+ with the intention of creating acceptor or donor states, respectively. The electrical conductivity of each powder was measured using the powder-solution-composite (PSC) method. The conductivity increased with the addition of Nb5+ from 3 similar to x similar to 10-3 similar to S/cm to 10 similar to x similar to 10-3 similar to S/cm in as-prepared powders, and from 0.3 similar to x similar to 10-3 similar to S/cm to 0.9 similar to x similar to 10-3 similar to S/cm in heat-treated powders (520 degrees C, 1 similar to h). In contrast, substitution with Ga3+ and Y3+ had no measureable effect on the material's conductivity. The lack of change with the addition of Ga3+ and Y3+, and relatively small increase upon Nb5+ addition is attributed to ionic compensation owing to the highly oxidizing nature of hydrothermal synthesis.
Extracted statement — chemical doping method: hydrothermal
Research field: Chemistry
Title: Preparation, Photocatalytic Activity, and Mechanism of Nano-TiO2 Co-Doped with Nitrogen and Iron (III)
Abstract: Nanoparticles of titanium dioxide co-doped with nitrogen and iron (III) were first prepared using the homogeneous precipitation-hydrothermal method. The structure and properties of the co-doped were studied by XRD, XPS, Raman, FL, and UV-diffuse reflectance spectra. By analyzing the structures and photocatalytic activities of the undoped and nitrogen and/or Fe3+-doped TiO2 under ultraviolet and visible light irradiation, the probable mechanism of co-doped particles was investigated. It is presumed that the nitrogen and Fe3+ ion doping induced the formation of new states closed to the valence band and conduction band, respectively. The co-operation of the nitrogen and Fe3+ ion leads to the much narrowing of the band gap and greatly improves the photocatalytic activity in the visible light region. Meanwhile, the co-doping can also promote the separation of the photogenerated electrons and holes to accelerate the transmission of photocurrent carrier. The photocatalyst co-doped with nitrogen and 0.5% Fe3+ sho...
Extracted statement — chemical doping method: hydrothermal
Research field: Computational Linguistics
Title: Generating Abstractive Summaries from Meeting Transcripts
Abstract: Summaries of meetings are very important as they convey the essential content of discussions in a concise form. Both participants and non-participants are interested in the summaries of meetings to plan for their future work. Generally, it is time consuming to read and understand the whole documents. Therefore, summaries play an important role as the readers are interested in only the important context of discussions. In this work, we address the task of meeting document summarization. Automatic summarization systems on meeting conversations developed so far have been primarily extractive, resulting in unacceptable summaries that are hard to read. The extracted utterances contain disfluencies that affect the quality of the extractive summaries. To make summaries much more readable, we propose an approach to generating abstractive summaries by fusing important content from several utterances. We first separate meeting transcripts into various topic segments, and then identify the important utterances in each segment using a supervised learning approach. The important utterances are then combined together to generate a one-sentence summary. In the text generation step, the dependency parses of the utterances in each segment are combined together to create a directed graph. The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment. The ILP formulation reduces disfluencies by leveraging grammatical relations that are more prominent in non-conversational style of text, and therefore generates summaries that is comparable to human-written abstractive summaries. Experimental results show that our method can generate more informative summaries than the baselines. In addition, readability assessments by human judges as well as log-likelihood estimates obtained from the dependency parser show that our generated summaries are significantly readable and well-formed.
Extracted statement — Summarization Type: Abstractive
Research field: Computational Linguistics
Title: Abstractive Meeting Summarization Using Dependency Graph Fusion
Abstract: Automatic summarization techniques on meeting conversations developed so far have been primarily extractive, resulting in poor summaries. To improve this, we propose an approach to generate abstractive summaries by fusing important content from several utterances. Any meeting is generally comprised of several discussion topic segments. For each topic segment within a meeting conversation, we aim to generate a one sentence summary from the most important utterances using an integer linear programming-based sentence fusion approach. Experimental results show that our method can generate more informative summaries than the baselines.
Extracted statement — Summarization Type: Abstractive
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Concept types: Complex
Research field: Computational Linguistics
Title: Domain-Independent Abstract Generation for Focused Meeting Summarization
Abstract: We address the challenge of generating natural language abstractive summaries for spoken meetings in a domain-independent fashion. We apply Multiple-Sequence Alignment to induce abstract generation templates that can be used for different domains. An Overgenerateand-Rank strategy is utilized to produce and rank candidate abstracts. Experiments using in-domain and out-of-domain training on disparate corpora show that our system uniformly outperforms state-of-the-art supervised extract-based approaches. In addition, human judges rate our system summaries significantly higher than compared systems in fluency and overall quality.
Extracted statement — Data Domain: Different
Research field: Computational Linguistics
Title: Extractive Summarization of Meeting Recordings
Abstract: Several approaches to automatic speech summarization are discussed below, using the ICSI Meetings corpus. We contrast feature-based approaches using prosodic and lexical features with maximal marginal relevance and latent semantic analysis approaches to summarization. While the latter two techniques are borrowed directly from the field of text summarization, feature-based approaches using prosodic information are able to utilize characteristics unique to speech data. We also investigate how the summarization results might deteriorate when carried out on ASR output as opposed to manual transcripts. All of the summaries are of an extractive variety, and are compared using the software ROUGE.
Extracted statement — Summarization Type: Extractive
Research field: Computational Linguistics
Title: The GENIA corpus: an annotated research abstract corpus in molecular biology domain
Abstract: With the information overload in genome-related field, there is an increasing need for natural language processing technology to extract information from literature and various attempts of information extraction using NLP has been being made. We are developing the necessary resources including domain ontology and annotated corpus from research abstracts in MEDLINE database (GENIA corpus). We are building the ontology and the corpus simultaneously, using each other. In this paper we report on our new corpus, its ontological basis, annotation scheme, and statistics of annotated objects. We also describe the tools used for corpus annotation and management.
Extracted statement — Concept types: Other
Research field: Computational Linguistics
Title: Construction of an annotated corpus to support biomedical information extraction
Abstract: Abstract Background Information Extraction (IE) is a component of text mining that facilitates knowledge discovery by automatically locating instances of interesting biomedical events from huge document collections. As events are usually centred on verbs and nominalised verbs, understanding the syntactic and semantic behaviour of these words is highly important. Corpora annotated with information concerning this behaviour can constitute a valuable resource in the training of IE components and resources. Results We have defined a new scheme for annotating sentence-bound gene regulation events, centred on both verbs and nominalised verbs. For each event instance, all participants ( arguments ) in the same sentence are identified and assigned a semantic role from a rich set of 13 roles tailored to biomedical research articles, together with a biological concept type linked to the Gene Regulation Ontology. To our knowledge, our scheme is unique within the biomedical field in terms of the range of event arguments identified. Using the scheme, we have created the Gene Regulation Event Corpus (GREC), consisting of 240 MEDLINE abstracts, in which events relating to gene regulation and expression have been annotated by biologists. A novel method of evaluating various different facets of the annotation task showed that average inter-annotator agreement rates fall within the range of 66% - 90%. Conclusion The GREC is a unique resource within the biomedical field, in that it annotates not only core relationships between entities, but also a range of other important details about these relationships, e.g., location, temporal, manner and environmental conditions. As such, it is specifically designed to support bio-specific tool and resource development. It has already been used to acquire semantic frames for inclusion within the BioLexicon (a lexical, terminological resource to aid biomedical text mining). Initial experiments have also shown that the corpus may viably be used to train IE components, such as semantic role labellers. The corpus and annotation guidelines are freely available for academic purposes.
Extracted statement — Semantic roles: Temporal
Research field: Computer and Systems Architecture
Title: A deep learning framework for character motion synthesis and editing
Abstract: We present a framework to synthesize character movements based on high level parameters, such that the produced movements respect the manifold of human motion, trained on a large motion capture dataset. The learned motion manifold, which is represented by the hidden units of a convolutional autoencoder, represents motion data in sparse components which can be combined to produce a wide range of complex movements. To map from high level parameters to the motion manifold, we stack a deep feedforward neural network on top of the trained autoencoder. This network is trained to produce realistic motion sequences from parameters such as a curve over the terrain that the character should follow, or a target location for punching and kicking. The feedforward control network and the motion manifold are trained independently, allowing the user to easily switch between feedforward networks according to the desired interface, without re-training the motion manifold. Once motion is generated it can be edited by performing optimization in the space of the motion manifold. This allows for imposing kinematic constraints, or transforming the style of the motion, while ensuring the edited motion remains natural. As a result, the system can produce smooth, high quality motion sequences without any manual pre-processing of the training data.
Extracted statement — Activity: Kicking
Research field: Computer Engineering
Title: Application of data anonymization in Learning Analytics
Abstract: Thanks to the proliferation of academic services on the Web and the opening of educational content, today, students can access a large number of free learning resources, and interact with value-added services. In this context, Learning Analytics can be carried out on a large scale thanks to the proliferation of open practices that promote the sharing of datasets. However, the opening or sharing of data managed through platforms and educational services, without considering the protection of users' sensitive data, could cause some privacy issues. Data anonymization is a strategy that should be adopted during lifecycle of data processing to reduce security risks. In this research, we try to characterize how much and how the anonymization techniques have been used in learning analytics proposals. From an initial exploration made in the Scopus database, we found that less than 6% of the papers focused on LA have also covered the privacy issue. Finally, through a specific case, we applied data anonymization and learning analytics to demonstrate that both technique can be integrated, in a reliably and effectively way, to support decision making in educational institutions.
Extracted statement — Publication Stage: Final
Research field: Computer Engineering
Title: Ontology of personal learning environments in the development of thesis project
Abstract: The thesis is the final step in academic formation of students. Its development may experience some difficulties that cause delays in delivery times. The internet allows students to access relevant and large amounts of information for use in the development of their theses. The internet also allows students to interact with others in order to create knowledge networks. However, exposure to too much information can produce infoxication. Therefore, there is a need to organise such information and technological resources. Through a personal learning environment (PLE), students can use current technology and online resources to develop their projects. Furthermore, by means of an ontological model, the underlying knowledge in the domain and environment can be represented in a readable format for machines. This paper presents an ontological model called PLET4Thesis, which has been designed in order to organise the process of thesis development using the elements required to create a PLE.
Extracted statement — Publication Stage: Final
Research field: Computer Engineering
Title: Systematic Search Process for Doctoral Theses in Centralized Repositories. A Study Case in the Context of Educational Innovation generated by ICT
Abstract: Educational innovation is a set of ideas, processes, and strategies applied in academic centers to improve teaching and learning processes. The application of Communication and Information Technologies in the field of educational innovation has been fundamental to promote changes that lead to the improvement of administrative and academic processes. However, some studies show the deficient and not very innovative use of technologies in the Latin American region. To determine the existing gap, the authors are executing a project that tries to know the current state of the art on this topic. This study corresponds to the first step of the project. Here, the authors describe the systematic search process designed to find the doctoral theses that have been developed on educational innovation generated by ICT. To meet this objective, the process has three phases: (1) identification of centralized repositories of doctoral theses in Spanish, (2) evaluation of selected repositories according to specific search criteria, and (3) selecting centralized repositories where it is possible to find theses on educational innovation and ICT. The analysis of 5 of the 222 repositories found indicates that each system offers different search characteristics, and the results show that the best option is to combine the sets of results since the theses come from different institutions. Finally, considering that the abilities of users to manage information systems can be diverse, providers and administrators of repositories should enhance their search services in such a way that all users can find and use the resources published.
Extracted statement — Publication Stage: Final
Research field: Computer Sciences
Title: TableSeer: automatic table metadata extraction and searching in digital libraries
Abstract: Tables are ubiquitous in digital libraries. In scientific documents, tables are widely used to present experimental results or statistical data in a condensed fashion. However, current search engines do not support table search. The difficulty of automatic extracting tables from un-tagged documents, the lack of a universal table metadata specification, and the limitation of the existing ranking schemes make table search problem challenging. In this paper, we describe TableSeer, a search engine for tables. TableSeer crawls digital libraries, detects tables from documents, extracts tables metadata, indexes and ranks tables, and provides a user-friendly search interface. We propose an extensive set of medium-independent metadata for tables that scientists and other users can adopt for representing table information. In addition, we devise a novel page box-cutting method to improve the performance of the table detection. Given a query, TableSeer ranks the matched tables using an innovative ranking algorithm - TableRank. TableRank rates each ⃭query, tableℂ pair with a tailored vector space model and a specific term weighting scheme. Overall, TableSeer eliminates the burden of manually extract table data from digital libraries and enables users to automatically examine tables. We demonstrate the value of TableSeer with empirical studies on scientific documents.
Extracted statement — Method automation: Automatic
Research field: Computer Sciences
Title: Deeper Task-Specificity Improves Joint Entity and Relation Extraction
Abstract: Multi-task learning (MTL) is an effective method for learning related tasks, but designing MTL models necessitates deciding which and how many parameters should be task-specific, as opposed to shared between tasks. We investigate this issue for the problem of jointly learning named entity recognition (NER) and relation extraction (RE) and propose a novel neural architecture that allows for deeper task-specificity than does prior work. In particular, we introduce additional task-specific bidirectional RNN layers for both the NER and RE tasks and tune the number of shared and task-specific layers separately for different datasets. We achieve state-of-the-art (SOTA) results for both tasks on the ADE dataset; on the CoNLL04 dataset, we achieve SOTA results on the NER task and competitive results on the RE task while using an order of magnitude fewer trainable parameters than the current SOTA architecture. An ablation study confirms the importance of the additional task-specific layers for achieving these results. Our work suggests that previous solutions to joint NER and RE undervalue task-specificity and demonstrates the importance of correctly balancing the number of shared and task-specific parameters for MTL approaches in general.
Extracted statement — has model: Deeper
Research field: Cultural History
Title: The Story of the Markham Car Collection: A Cross-Platform Panoramic Tour of Contested Heritage
Abstract: In this article, we share our experiences of using digital technologies and various media to present historical narratives of a museum object collection aiming to provide an engaging experience on multiple platforms. Based on P. Joseph’s article, Dawson presented multiple interpretations and historical views of the Markham car collection across various platforms using multimedia resources. Through her creative production, she explored how to use cylindrical panoramas and rich media to offer new ways of telling the controversial story of the contested heritage of a museum’s veteran and vintage car collection. The production’s usability was investigated involving five experts before it was published online and the general users’ experience was investigated. In this article, we present an important component of findings which indicates that virtual panorama tours featuring multimedia elements could be successful in attracting new audiences and that using this type of storytelling technique can be effective in the museum sector. The storyteller panorama tour presented here may stimulate GLAM (galleries, libraries, archives, and museums) professionals to think of new approaches, implement new strategies or services to engage their audiences more effectively. The research may ameliorate the education of future professionals as well.
Extracted statement — has stakeholder: Professional
Research field: Databases/Information Systems
Title: A Real-time Heuristic-based Unsupervised Method for Name Disambiguation in Digital Libraries
Abstract: This paper addresses the problem of name disambiguation in the context of digital libraries that administer bibliographic citations. The problem occurs when multiple authors share a common name or when multiple name variations for an author appear in citation records. Name disambiguation is not a trivial task, and most digital libraries do not provide an ecient way to accurately identify the citation records for an author. Furthermore, lack of complete meta-data information in digital libraries hinders the development of a generic algorithm that can be applicable to any dataset. We propose a heuristic-based, unsupervised and adaptive method that also examines users’ interactions in order to include users’ feedback in the disambiguation process. Moreover, the method exploits important features associated with author and citation records, such as co-authors, aliation, publication title, venue, etc., creating a multilayered hierarchical clustering algorithm which transforms itself according to the available information, and forms clusters of unambiguous records. Our experiments on a set of researchers’ names considered to be highly ambiguous produced high precision and recall results, and decisively armed the viability of our algorithm.
Extracted statement — Method: Heuristic-based