Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Nanoscience and Nanotechnology
Title: Strain‐Induced Band‐Gap Tuning of 2D‐SnSSe Flakes for Application in Flexible Sensors
Abstract: Flexible strain‐sensitive‐material‐based sensors are desired owing to their widespread applications in intelligent robots, health monitoring, human motion detection, and other fields. High electrical–mechanical coupling behaviors of 2D materials make them one of the most promising candidates for miniaturized, integrated, and high‐resolution strain sensors, motivating to explore the influence of strain‐induced band‐gap changes on electrical properties of more materials and assess their potential application in strain sensors. Herein, a ternary SnSSe alloy nanosheet‐based strain sensor is reported showing an enhanced gauge factor (GF) up to 69.7 and a good reproducibility and linearity within strain of 0.9%. Such sensor holds high‐sensitive features under low strain, and demonstrates an improved sensitivity with a decrease in the membrane thickness. The high sensitivity is attributed to widening band gap and density of states reduction induced by strain, as verified by theoretical model and first‐principles calculations. These findings show that a sensor with adjustable strain sensitivity might be realized by simply changing the elemental constituents of 2D alloying materials.
Extracted statement — Material: SnSSe
Research field: Nanoscience and Nanotechnology
Title: Stretchable, Transparent, Ultrasensitive, and Patchable Strain Sensor for Human–Machine Interfaces Comprising a Nanohybrid of Carbon Nanotubes and Conductive Elastomers
Abstract: UNLABELLED Interactivity between humans and smart systems, including wearable, body-attachable, or implantable platforms, can be enhanced by realization of multifunctional human-machine interfaces, where a variety of sensors collect information about the surrounding environment, intentions, or physiological conditions of the human to which they are attached. Here, we describe a stretchable, transparent, ultrasensitive, and patchable strain sensor that is made of a novel sandwich-like stacked piezoresisitive nanohybrid film of single-wall carbon nanotubes (SWCNTs) and a conductive elastomeric composite of polyurethane (PU)-poly(3,4-ethylenedioxythiophene) polystyrenesulfonate ( PEDOT PSS). This sensor, which can detect small strains on human skin, was created using environmentally benign water-based solution processing. We attributed the tunability of strain sensitivity (i.e., gauge factor), stability, and optical transparency to enhanced formation of percolating networks between conductive SWCNTs and PEDOT phases at interfaces in the stacked PU-PEDOT:PSS/SWCNT/PU-PEDOT:PSS structure. The mechanical stability, high stretchability of up to 100%, optical transparency of 62%, and gauge factor of 62 suggested that when attached to the skin of the face, this sensor would be able to detect small strains induced by emotional expressions such as laughing and crying, as well as eye movement, and we confirmed this experimentally.
Extracted statement — Device Location: Face
Research field: Natural Language Processing
Title: PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track
Abstract: One of the biomedical entity types of relevance for medicine or biosciences are chemical compounds and drugs. The correct detection these entities is critical for other text mining applications building on them, such as adverse drug-reaction detection, medication-related fake news or drug-target extraction. Although a significant effort was made to detect mentions of drugs/chemicals in English texts, so far only very limited attempts were made to recognize them in medical documents in other languages. Taking into account the growing amount of medical publications and clinical records written in Spanish, we have organized the first shared task on detecting drug and chemical entities in Spanish medical documents. Additionally, we included a clinical concept-indexing sub-track asking teams to return SNOMED-CT identifiers related to drugs/chemicals for a collection of documents. For this task, named PharmaCoNER, we generated annotation guidelines together with a corpus of 1,000 manually annotated clinical case studies. A total of 22 teams participated in the sub-track 1, (77 system runs), and 7 teams in the sub-track 2 (19 system runs). Top scoring teams used sophisticated deep learning approaches yielding very competitive results with F-measures above 0.91. These results indicate that there is a real interest in promoting biomedical text mining efforts beyond English. We foresee that the PharmaCoNER annotation guidelines, corpus and participant systems will foster the development of new resources for clinical and biomedical text mining systems of Spanish medical data.
Extracted statement — Concept types: drug
Research field: Natural Language Processing
Title: PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track
Abstract: One of the biomedical entity types of relevance for medicine or biosciences are chemical compounds and drugs. The correct detection these entities is critical for other text mining applications building on them, such as adverse drug-reaction detection, medication-related fake news or drug-target extraction. Although a significant effort was made to detect mentions of drugs/chemicals in English texts, so far only very limited attempts were made to recognize them in medical documents in other languages. Taking into account the growing amount of medical publications and clinical records written in Spanish, we have organized the first shared task on detecting drug and chemical entities in Spanish medical documents. Additionally, we included a clinical concept-indexing sub-track asking teams to return SNOMED-CT identifiers related to drugs/chemicals for a collection of documents. For this task, named PharmaCoNER, we generated annotation guidelines together with a corpus of 1,000 manually annotated clinical case studies. A total of 22 teams participated in the sub-track 1, (77 system runs), and 7 teams in the sub-track 2 (19 system runs). Top scoring teams used sophisticated deep learning approaches yielding very competitive results with F-measures above 0.91. These results indicate that there is a real interest in promoting biomedical text mining efforts beyond English. We foresee that the PharmaCoNER annotation guidelines, corpus and participant systems will foster the development of new resources for clinical and biomedical text mining systems of Spanish medical data.
Extracted statement — Entity types: Drug
Research field: Natural Language Processing
Title: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Abstract: Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.
Extracted statement — Evaluation metrics: Accuracy
Research field: Natural Language Processing
Title: Overview of the Bacteria Biotope Task at BioNLP Shared Task 2016
Abstract: This paper presents the Bacteria Biotope task of the BioNLP Shared Task 2016, which follows the previous 2013 and 2011 editions. The task focuses on the extraction of the locations (biotopes and geographical places) of bacteria from PubMe abstracts and the characterization of bacteria and their associated habitats with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on bacteria habitats for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, the challenge organization, and the evaluation metrics. We also provide an analysis of the results obtained by participants.
Extracted statement — Concept types: Bacteria
Research field: Natural Language Processing
Title: Overview of the Bacteria Biotope Task at BioNLP Shared Task 2016
Abstract: This paper presents the Bacteria Biotope task of the BioNLP Shared Task 2016, which follows the previous 2013 and 2011 editions. The task focuses on the extraction of the locations (biotopes and geographical places) of bacteria from PubMe abstracts and the characterization of bacteria and their associated habitats with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on bacteria habitats for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, the challenge organization, and the evaluation metrics. We also provide an analysis of the results obtained by participants.
Extracted statement — Entity types: Bacteria
Research field: Natural Language Processing
Title: BioNLP shared Task 2013 – An Overview of the Bacteria Biotope Task
Abstract: This paper presents the Bacteria Biotope task of the BioNLP Shared Task 2013, which follows BioNLP-ST-11. The Bacteria Biotope task aims to extract the location of bacteria from scientific web pages and to characterize these locations with respect to the OntoBiotope ontology. Bacteria locations are crucil knowledge in biology for phenotype studies. The paper details the corpus specifications, the evaluation metrics, and it summarizes and discusses the participant results.
Extracted statement — Entity types: Bacteria
Research field: Natural Language Processing
Title: Overview of the Epigenetics and Post-translational Modifications (EPI) task of BioNLP Shared Task 2011
Abstract: This paper presents the preparation, resources, results and analysis of the Epigenetics and Post-translational Modifications (EPI) task, a main task of the BioNLP Shared Task 2011. The task concerns the extraction of detailed representations of 14 protein and DNA modification events, the catalysis of these reactions, and the identification of instances of negated or speculatively stated event instances. Seven teams submitted final results to the EPI task in the shared task, with the highest-performing system achieving 53% F-score in the full task and 69% F-score in the extraction of a simplified set of core event arguments.
Extracted statement — Event / Relation Types: Catalysis
Research field: Natural Language Processing
Title: Overview of the Epigenetics and Post-translational Modifications (EPI) task of BioNLP Shared Task 2011
Abstract: This paper presents the preparation, resources, results and analysis of the Epigenetics and Post-translational Modifications (EPI) task, a main task of the BioNLP Shared Task 2011. The task concerns the extraction of detailed representations of 14 protein and DNA modification events, the catalysis of these reactions, and the identification of instances of negated or speculatively stated event instances. Seven teams submitted final results to the EPI task in the shared task, with the highest-performing system achieving 53% F-score in the full task and 69% F-score in the extraction of a simplified set of core event arguments.
Extracted statement — Event types: Catalysis
Research field: Natural Language Processing
Title: ClearTK-TimeML: A minimalist approach to TempEval 2013
Abstract: The ClearTK-TimeML submission to TempEval 2013 competed in all English tasks: identifying events, identifying times, and identifying temporal relations. The system is a pipeline of machine-learning models, each with a small set of features from a simple morpho-syntactic annotation pipeline, and where temporal relations are only predicted for a small set of syntactic constructions and relation types. ClearTKTimeML ranked 1 st for temporal relation F1, time extent strict F1 and event tense accuracy.
Extracted statement — Tool name: ClearTK-TimeML
Research field: Natural Language Processing
Title: CrossNER: Evaluating Cross-Domain Named Entity Recognition
Abstract: Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. Additionally, we also provide a domain-related corpus since using it to continue pre-training language models (domain-adaptive pre-training) is effective for the domain adaptation. We then conduct comprehensive experiments to explore the effectiveness of leveraging different levels of the domain corpus and pre-training strategies to do domain-adaptive pre-training for the cross-domain task. Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the NER domain adaptation, and our proposed method can consistently outperform existing cross-domain NER baselines. Nevertheless, experiments also illustrate the challenge of this cross-domain NER task. We hope that our dataset and baselines will catalyze research in the NER domain adaptation area. The code and data are available at this https URL.
Extracted statement — Dataset name: CrossNER
Research field: Natural Language Processing
Title: The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources
Abstract: We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.
Extracted statement — Concept types: Data
Research field: Natural Language Processing
Title: SciREX: A Challenge Dataset for Document-Level Information Extraction
Abstract: Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https://github.com/allenai/SciREX .
Extracted statement — Concept types: Dataset
Research field: Natural Language Processing
Title: Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
Abstract: While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.
Extracted statement — Concept types: Dataset
Research field: Natural Language Processing
Title: Identifying used methods and datasets in scientific publications
Abstract: Although it has become common to assess publications and researchers by means of their citation count (e.g., using the h-index), measuring the impact of scientific methods and datasets (e.g., using an “h-index for datasets”) has been performed only to a limited extent. This is not surprising because the usage information of methods and datasets is typically not explicitly provided by the authors, but hidden in a publication’s text. In this paper, we propose an approach to identifying methods and datasets in texts that have actually been used by the authors. Our approach first recognizes datasets and methods in the text by means of a domain-specific named entity recognition method with minimal human interaction. It then classifies these mentions into used vs. non-used based on the textual contexts. The obtained labels are aggregated on the document level and integrated into the Microsoft Academic Knowledge Graph modeling publications’ metadata. In experiments based on the Microsoft Academic Graph, we show that both method and dataset mentions can be identified and correctly classified with respect to their usage to a high degree. Overall, our approach facilitates method and dataset recommendation, enhanced paper recommendation, and scientific impact quantification. It can be extended in such a way that it can identify mentions of any entity type (e.g., task).
Extracted statement — Concept types: Dataset
Research field: Natural Language Processing
Title: Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task
Abstract: Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task—a result that approaches the human inter-annotator agreement (0.8875)—and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system’s ability to return real-time results: the average response time for each team’s DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
Extracted statement — Coarse-grained Entity type: Disease