Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Artificial Intelligence
Title: SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus
Abstract: Research on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered. In this work, we present DeftEval, a SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflects the complex reality of definitions in natural language. Definitions and glosses in free text often appear without explicit indicators, across sentences boundaries, or in an otherwise complex linguistic manner. DeftEval involved 3 distinct subtasks: 1) Sentence classification, 2) sequence labeling, and 3) relation extraction.
Extracted statement — has research problem: Sentence classification
Research field: Artificial Intelligence
Title: SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus
Abstract: Research on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered. In this work, we present DeftEval, a SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflects the complex reality of definitions in natural language. Definitions and glosses in free text often appear without explicit indicators, across sentences boundaries, or in an otherwise complex linguistic manner. DeftEval involved 3 distinct subtasks: 1) Sentence classification, 2) sequence labeling, and 3) relation extraction.
Extracted statement — has research problem: Sequence labeling
Research field: Artificial Intelligence
Title: SemEval-2012 Task 3: Spatial Role Labeling
Abstract: Many NLP applications require information about locations of objects referenced in text, or relations between them in space. For example, the phrase a book on the desk contains information about the location of the object book, as trajector, with respect to another object desk, as landmark. Spatial Role Labeling (SpRL) is an evaluation task in the information extraction domain which sets a goal to automatically process text and identify objects of spatial scenes and relations between them. This paper describes the task in Semantic Evaluations 2013, annotation schema, corpora, participants, methods and results obtained by the participants.
Extracted statement — has research problem: Spatial Role Labeling
Research field: Artificial Intelligence
Title: Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain
Abstract: The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world consumer health queries, (ii) a multi-answer summarization task that targeted aggregation of multiple relevant answers to a biomedical question into one concise and relevant answer, and (iii) a radiology report summarization task addressing the development of clinically relevant impressions from radiology report findings. Thirty-five teams participated in these shared tasks with sixteen working notes submitted (fifteen accepted) describing a wide variety of models developed and tested on the shared and external datasets. In this paper, we describe the tasks, the datasets, the models and techniques developed by various teams, the results of the evaluation, and a study of correlations among various summarization evaluation measures. We hope that these shared tasks will bring new research and insights in biomedical text summarization and evaluation.
Extracted statement — has research problem: Summarization
Research field: Artificial Intelligence
Title: A Neural Approach for Text Extraction from Scholarly Figures
Abstract: In recent years, the problem of scene text extraction from images has received extensive attention and significant progress. However, text extraction from scholarly figures such as plots and charts remains an open problem, in part due to the difficulty of locating irregularly placed text lines. To the best of our knowledge, literature has not described the implementation of a text extraction system for scholarly figures that adapts deep convolutional neural networks used for scene text detection. In this paper, we propose a text extraction approach for scholarly figures that forgoes preprocessing in favor of using a deep convolutional neural network for text line localization. Our system uses a publicly available scene text detection approach whose network architecture is well suited to text extraction from scholarly figures. Training data are derived from charts in arXiv papers which are extracted using Allen Institute's pdffigures tool. Since this tool analyzes PDF data as a container format in order to extract text location through the mechanisms which render it, we were able to gather a large set of labeled training samples. We show significant improvement from methods in the literature, and discuss the structural changes of the text extraction pipeline.
Extracted statement — has research problem: text extraction from images
Research field: Artificial Intelligence
Title: A Novel Hierarchical Binary Tagging Framework for Joint Extraction of Entities and Relations
Abstract: Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same entities. We propose a novel Hierarchical Binary Tagging (HBT) framework derived from a principled problem formulation. Instead of treating relations as discrete labels as in previous works, our new framework models relations as functions that map subjects to objects in a sentence, which naturally handles overlapping triples. Experiments show that the proposed framework already outperforms state-of-the-art methods even its encoder module uses a randomly initialized BERT encoder, showing the power of the new tagging framework. It enjoys further performance boost when employing a pretrained BERT encoder, outperforming the strongest baseline by 25.6 and 45.9 absolute gain in F1-score on two public datasets NYT and WebNLG, respectively. In-depth analysis on different types of overlapping triples shows that the method delivers consistent performance gain in all scenarios.
Extracted statement — has research problem: the overlapping triple problem where multiple relational triples in the same sentence share the same entities
Research field: Artificial Intelligence
Title: SemEval-2007 Task 12: Turkish Lexical Sample Task
Abstract: This paper presents the task definition, resources, and the single participant system for Task 12: Turkish Lexical Sample Task (TLST), which was organized in the SemEval-2007 evaluation exercise. The methodology followed for developing the specific linguistic resources necessary for the task has been described in this context. A language-specific feature set was defined for Turkish. TLST consists of three pieces of data: The dictionary, the training data, and the evaluation data. Finally, a single system that utilizes a simple statistical method was submitted for the task and evaluated.
Extracted statement — has research problem: Turkish Lexical Sample Task
Research field: Artificial Intelligence
Title: UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
Abstract: In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
Extracted statement — has research problem: Unsupervised Lexical Semantic Change Detection
Research field: Artificial Intelligence
Title: The SemEval-2007 WePS Evaluation: Establishing a benchmark for the Web People Search Task
Abstract: This paper presents the task definition, resources, participation, and comparative results for the Web People Search task, which was organized as part of the SemEval-2007 evaluation exercise. This task consists of clustering a set of documents that mention an ambiguous person name according to the actual entities referred to using that name.
Extracted statement — has research problem: Web People Search task
Research field: Arts and Humanities
Title: Are the FAIR Data Principles fair?
Abstract: This practice paper describes an ongoing research project to test the effectiveness and relevance of the FAIR Data Principles. Simultaneously, it will analyse how easy it is for data archives to adhere to the principles. The research took place from November 2016 to January 2017, and will be underpinned with feedback from the repositories. The FAIR Data Principles feature 15 facets corresponding to the four letters of FAIR - Findable, Accessible, Interoperable, Reusable. These principles have already gained traction within the research world. The European Commission has recently expanded its demand for research to produce open data. The relevant guidelines1are explicitly written in the context of the FAIR Data Principles. Given an increasing number of researchers will have exposure to the guidelines, understanding their viability and suggesting where there may be room for modification and adjustment is of vital importance. This practice paper is connected to a dataset(Dunning et al.,2017) containing the original overview of the sample group statistics and graphs, in an Excel spreadsheet. Over the course of two months, the web-interfaces, help-pages and metadata-records of over 40 data repositories have been examined, to score the individual data repository against the FAIR principles and facets. The traffic-light rating system enables colour-coding according to compliance and vagueness. The statistical analysis provides overall, categorised, on the principles focussing, and on the facet focussing results. The analysis includes the statistical and descriptive evaluation, followed by elaborations on Elements of the FAIR Data Principles, the subject specific or repository specific differences, and subsequently what repositories can do to improve their information architecture.
Extracted statement — has research problem: effectiveness and relevance of the FAIR Data Principles
Research field: Bioinformatics
Title: PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping
Abstract: <jats:title>Abstract</jats:title>
               <jats:p>Comparative phylogenetics has been largely lacking a method for reconstructing the evolution of phenotypic entities that consist of ensembles of multiple discrete traits—entire organismal anatomies or organismal body regions. In this study, we provide a new approach named PARAMO (PhylogeneticAncestralReconstruction ofAnatomy byMappingOntologies) that appropriately models anatomical dependencies and uses ontology-informed amalgamation of stochastic maps to reconstruct phenotypic evolution at different levels of anatomical hierarchy including entire phenotypes. This approach provides new opportunities for tracking phenotypic radiations and evolution of organismal anatomies.</jats:p>
Extracted statement — has research problem: Comparative phylogenetics
Research field: Bioinformatics
Title: Deep learning improves prediction of drug–drug and drug–food interactions
Abstract: Significance Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent interactions, can trigger unexpected pharmacological effects such as adverse drug events (ADEs). Several existing methods predict drug interactions, but require detailed, but often unavailable drug information as inputs, such as drug targets. To this end, we present a computational framework DeepDDI that accurately predicts DDI types for given drug pairs and drug–food constituent pairs using only name and structural information as inputs. We show four applications of DeepDDI to better understand drug interactions, including prediction of DDI mechanisms causing ADEs, suggestion of alternative drug members for the intended pharmacological effects without negative health effects, prediction of the effects of food constituents on interacting drugs, and prediction of bioactivities of food constituents. Drug interactions, including drug–drug interactions (DDIs) and drug–food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Several computational methods have been developed to better understand drug interactions, especially for DDIs. However, these methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. Here, we report development of a computational framework DeepDDI that uses names of drug–drug or drug–food constituent pairs and their structural information as inputs to accurately generate 86 important DDI types as outputs of human-readable sentences. DeepDDI uses deep neural network with its optimized prediction performance and predicts 86 DDI types with a mean accuracy of 92.4% using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. DeepDDI is used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects. Furthermore, DeepDDI is applied to 3,288,157 drug–food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents are predicted. These results suggest that DeepDDI can provide important information on drug prescription and even dietary suggestions while taking certain drugs and also guidelines during drug development.
Extracted statement — has research problem: DDI prediction
Research field: Bioinformatics
Title: Classifying semantic relations in bioscience texts
Abstract: A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy.
Extracted statement — has research problem: Identification of semantic relations
Research field: Bioinformatics
Title: Exploiting syntax when detecting protein names in text
Abstract: This paper presents work on a method to detect names of proteins in running text. Our system - Yapex - uses a combination of lexical and syntactic knowledge, heuristic filters and a local dynamic dictionary. The syntactic information given by a general-purpose off-the-shelf parser supports the correct identification of the boundaries of protein names, and the local dynamic dictionary finds protein names in positions incompletely analysed by the parser. We present the different steps involved in our approach to protein tagging, and show how combinations of them influence recall and precision. We evaluate the system on a corpus of MEDLINE abstracts and compare it with the KeX system (Fukuda et al., 1998) along four different notions of correctness.
Extracted statement — has research problem: Protein tagging
Research field: Bioinformatics
Title: LINNAEUS: A species name identification system for biomedical literature
Abstract: Abstract Background The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles. Results In this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers. Conclusions LINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://linnaeus.sourceforge.net/" ext-link-type="uri">http://linnaeus.sourceforge.net/</jats:ext-link>.
Extracted statement — has research problem: Species name recognition and normalization
Research field: Biomedical Engineering and Bioengineering
Title: Tensor gradient based discriminative region analysis for cognitive state classification
Abstract: Extraction of relevant features from high-dimensional multi-way functional MRI (fMRI) data is essential for the classification of a cognitive task. In general, fMRI records a combination of neural activation signals and several other noisy components. Alternatively, fMRI data is represented as a high dimensional array using a number of voxels, time instants, and snapshots. The organisation of fMRI data includes a number of Region Of Interests (ROI), snapshots, and thousand of voxels. The crucial step in cognitive task classification is a reduction of feature size through feature selection. Extraction of a specific pattern of interest within the noisy components is a challenging task. Tensor decomposition techniques have found several applications in the scientific fields. In this paper, a novel tensor gradient-based feature extraction technique for cognitive task classification is proposed. The technique has efficiently been applied on StarPlus fMRI data. Also, the technique has been used to discriminate the ROIs in fMRI data in terms of cognitive state classification. The method has been achieved a better average accuracy when compared to other existing feature extraction methods.
Extracted statement — has research problem: Cognitive state classification
Research field: Botany
Title: Difference in reproductive mode rather than ploidy explains niche differentiation in sympatric sexual and apomictic populations of
            Potentilla puberula
Abstract: Abstract Apomicts tend to have larger geographical distributional ranges and to occur in ecologically more extreme environments than their sexual progenitors. However, the expression of apomixis is typically linked to polyploidy. Thus, it is a priori not clear whether intrinsic effects related to the change in the reproductive mode or rather in the ploidy drive ecological differentiation. We used sympatric sexual and apomictic populations of Potentilla puberula to test for ecological differentiation. To distinguish the effects of reproductive mode and ploidy on the ecology of cytotypes, we compared the niches (a) of sexuals (tetraploids) and autopolyploid apomicts (penta‐, hepta‐, and octoploids) and (b) of the three apomictic cytotypes. We based comparisons on a ploidy screen of 238 populations along a latitudinal transect through the Eastern European Alps and associated bioclimatic, and soil and topographic data. Sexual tetraploids preferred primary habitats at drier, steeper, more south‐oriented slopes, while apomicts mostly occurred in human‐made habitats with higher water availability. Contrariwise, we found no or only marginal ecological differentiation among the apomictic higher ploids. Based on the pronounced ecological differences found between sexuals and apomicts, in addition to the lack of niche differentiation among cytotypes of the same reproductive mode, we conclude that reproductive mode rather than ploidy is the main driver of the observed differences. Moreover, we compared our system with others from the literature, to stress the importance of identifying alternative confounding effects (such as hybrid origin). Finally, we underline the relevance of studying ecological parthenogenesis in sympatry, to minimize the effects of differential migration abilities.
Extracted statement — has research problem: ecological parthenogenesis
Research field: Communication Neuroscience
Title: Engaged listeners: shared neural processing of powerful political speeches
Abstract: Powerful speeches can captivate audiences, whereas weaker speeches fail to engage their listeners. What is happening in the brains of a captivated audience? Here, we assess audience-wide functional brain dynamics during listening to speeches of varying rhetorical quality. The speeches were given by German politicians and evaluated as rhetorically powerful or weak. Listening to each of the speeches induced similar neural response time courses, as measured by inter-subject correlation analysis, in widespread brain regions involved in spoken language processing. Crucially, alignment of the time course across listeners was stronger for rhetorically powerful speeches, especially for bilateral regions of the superior temporal gyri and medial prefrontal cortex. Thus, during powerful speeches, listeners as a group are more coupled to each other, suggesting that powerful speeches are more potent in taking control of the listeners' brain responses. Weaker speeches were processed more heterogeneously, although they still prompted substantially correlated responses. These patterns of coupled neural responses bear resemblance to metaphors of resonance, which are often invoked in discussions of speech impact, and contribute to the literature on auditory attention under natural circumstances. Overall, this approach opens up possibilities for research on the neural mechanisms mediating the reception of entertaining or persuasive messages.
Extracted statement — has research problem: brain dynamics during listening to speeches