Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Natural Language Processing
Title: Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine
Abstract: Abstract The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and was organized in two tasks: (i) document triage task, focused on identifying scientific literature containing experimentally verified protein–protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs). To assist system developers and task participants, a large-scale corpus of PubMed documents was manually annotated for this task. Ten teams worldwide contributed 22 distinct text-mining models for the document triage task, and six teams worldwide contributed 14 different text-mining systems for the relation extraction task. When comparing the text-mining system predictions with human annotations, for the triage task, the best F-score was 69.06%, the best precision was 62.89%, the best recall was 98.0% and the best average precision was 72.5%. For the relation extraction task, when taking homologous genes into account, the best F-score was 37.73%, the best precision was 46.5% and the best recall was 54.1%. Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods. Given the level of participation and the individual team results we find the precision medicine track to be successful in engaging the text-mining research community. In the meantime, the track produced a manually annotated corpus of 5509 PubMed documents developed by BioGRID curators and relevant for precision medicine. The data set is freely available to the community, and the specific interactions have been integrated into the BioGRID data set. In addition, this challenge provided the first results of automatically identifying PubMed articles that describe PPI affected by mutations, as well as extracting the affected relations from those articles. Still, much progress is needed for computer-assisted precision medicine text mining to become mainstream. Future work should focus on addressing the remaining technical challenges and incorporating the practical benefits of text-mining tools into real-world precision medicine information-related curation.
Extracted statement — subtasks: relation extraction task
Research field: Natural Language Processing
Title: Extraction of Information from the Text of Chemical Patents. 1. Identification of Specific Chemical Names
Abstract: Much attention has been paid to translating isolated chemical names into forms such as connection tables, but less effort has been expended in identifying substance names in running text to make them available for processing. The requirement for automatic name identification becomes a more urgent priority today, not the least in light of the inherent importance of patents and the increasing complexity of newly synthesized substances and, with these, the need for error-free processing of information from patent and other documents. The elaboration of a methodology for isolating substance names in the text of English-language patents is described here, using, in part, the SGML (Standard Generalized Markup Language) of the patent text as an aid to this process. Evaluation of the procedures, which are still at an early stage of development, demonstrates that even simple methods can achieve very high degrees of success.
Extracted statement — Data formats: SGML (Standard Generalized Markup Language)
Research field: Natural Language Processing
Title: BioCreative VII–Task 3: Automatic Extraction of Medication Names in Tweets
Abstract: We present the BioCreative VII Task 3 which focuses on drug names extraction from tweets. Recognized to provide unique insights into population health, detecting health related tweets is notoriously challenging for natural language processing tools. Tweets are written about any and all topics, most of them not related to health. Additionally, they are written with little regard for proper grammar, are inherently colloquial, and are almost never proof-read. Given a tweet, task 3 consists of detecting if the tweet has a mention of a drug name and, if so, extracting the span of the drug mention. We made available 182,049 tweets publicly posted by 212 Twitter users with all drugs mentions manually annotated. This corpus exhibits the natural and strongly imbalanced distribution of positive tweets, with only 442 tweets (0.2%) mentioning a drug. This task was an opportunity for participants to evaluate methods robust to classimbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. We summarize the corpus and the tools created for the challenge, which is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreativevii/track-3/. We analyze the methods and the results of the competing systems with a focus on learning from classimbalanced data. Keywords—social media; pharmacovigilance; named entity recognition; drug name extraction; class-imbalance.
Extracted statement — Data domains: Social media
Research field: Natural Language Processing
Title: The Stanford CoreNLP Natural Language Processing Toolkit
Abstract: We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.
Extracted statement — Tool: Stanford CoreNLP
Research field: Natural Language Processing
Title: The Stanford CoreNLP Natural Language Processing Toolkit
Abstract: We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.
Extracted statement — Tool name: Stanford CoreNLP toolkit
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 — Dataset name: STEM-ECR v1.0 dataset
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 — Data coverage: Web pages
Research field: Natural Language Processing
Title: Named Entity Recognition with Bidirectional LSTM-CNNs
Abstract: Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.
Extracted statement — Result: F1 score
Research field: Numerical Analysis/Scientific Computing
Title: ACM-BCB '17 Tutorial: Robotics-inspired Algorithms for Modeling Protein Structures and Motions
Abstract: With biomolecular structure recognized as central to understanding mechanisms in the cell, computational chemists and biophysicists have spent significant efforts on modeling structure and dynamics. While significant advances have been made, particularly in the design of sophisticated energetic models and molecular representations, such efforts are experiencing diminishing returns. One of the culprits is low exploration capability. The impasse has attracted AI researchers to offer adaptations of robot motion planning algorithms for modeling biomolecular structures and motions. This tutorial introduces students and researchers to robotics-inspired treatments and methodologies for understanding and elucidating the role of structure and dynamics in the function of biomolecules. The presentation is enhanced via an open-source software developed in the Shehu Computational Biology laboratory. The software allows researchers to integrate themselves in a new research domain and drive further research via plug-and-play capabilities. The hands-on approach in the the tutorial benefits both students and senior researchers keen to make contributions in computational structural biology.
Extracted statement — Material: Computational Structural Biology
Research field: Numerical Analysis/Scientific Computing
Title: Understanding, Categorizing and Predicting Semantic Image-Text Relations
Abstract: Two modalities are often used to convey information in a complementary and beneficial manner, e.g., in online news, videos, educational resources, or scientific publications. The automatic understanding of semantic correlations between text and associated images as well as their interplay has a great potential for enhanced multimodal web search and recommender systems. However, automatic understanding of multimodal information is still an unsolved research problem. Recent approaches such as image captioning focus on precisely describing visual content and translating it to text, but typically address neither semantic interpretations nor the specific role or purpose of an image-text constellation. In this paper, we go beyond previous work and investigate, inspired by research in visual communication, useful semantic image-text relations for multimodal information retrieval. We derive a categorization of eight semantic image-text classes (e.g., "illustration" or "anchorage") and show how they can systematically be characterized by a set of three metrics: cross-modal mutual information, semantic correlation, and the status relation of image and text. Furthermore, we present a deep learning system to predict these classes by utilizing multimodal embeddings. To obtain a sufficiently large amount of training data, we have automatically collected and augmented data from a variety of datasets and web resources, which enables future research on this topic. Experimental results on a demanding test set demonstrate the feasibility of the approach.
Extracted statement — Has metric: Cross-Modal Mutual Information
Research field: Numerical Analysis/Scientific Computing
Title: Understanding, Categorizing and Predicting Semantic Image-Text Relations
Abstract: Two modalities are often used to convey information in a complementary and beneficial manner, e.g., in online news, videos, educational resources, or scientific publications. The automatic understanding of semantic correlations between text and associated images as well as their interplay has a great potential for enhanced multimodal web search and recommender systems. However, automatic understanding of multimodal information is still an unsolved research problem. Recent approaches such as image captioning focus on precisely describing visual content and translating it to text, but typically address neither semantic interpretations nor the specific role or purpose of an image-text constellation. In this paper, we go beyond previous work and investigate, inspired by research in visual communication, useful semantic image-text relations for multimodal information retrieval. We derive a categorization of eight semantic image-text classes (e.g., "illustration" or "anchorage") and show how they can systematically be characterized by a set of three metrics: cross-modal mutual information, semantic correlation, and the status relation of image and text. Furthermore, we present a deep learning system to predict these classes by utilizing multimodal embeddings. To obtain a sufficiently large amount of training data, we have automatically collected and augmented data from a variety of datasets and web resources, which enables future research on this topic. Experimental results on a demanding test set demonstrate the feasibility of the approach.
Extracted statement — Has implementation: Multimodal Embedding
Research field: Numerical Analysis/Scientific Computing
Title: Understanding, Categorizing and Predicting Semantic Image-Text Relations
Abstract: Two modalities are often used to convey information in a complementary and beneficial manner, e.g., in online news, videos, educational resources, or scientific publications. The automatic understanding of semantic correlations between text and associated images as well as their interplay has a great potential for enhanced multimodal web search and recommender systems. However, automatic understanding of multimodal information is still an unsolved research problem. Recent approaches such as image captioning focus on precisely describing visual content and translating it to text, but typically address neither semantic interpretations nor the specific role or purpose of an image-text constellation. In this paper, we go beyond previous work and investigate, inspired by research in visual communication, useful semantic image-text relations for multimodal information retrieval. We derive a categorization of eight semantic image-text classes (e.g., "illustration" or "anchorage") and show how they can systematically be characterized by a set of three metrics: cross-modal mutual information, semantic correlation, and the status relation of image and text. Furthermore, we present a deep learning system to predict these classes by utilizing multimodal embeddings. To obtain a sufficiently large amount of training data, we have automatically collected and augmented data from a variety of datasets and web resources, which enables future research on this topic. Experimental results on a demanding test set demonstrate the feasibility of the approach.
Extracted statement — Has metric: Semantic Correlation
Research field: Numerical Analysis/Scientific Computing
Title: ACM-BCB '17 Tutorial: Robotics-inspired Algorithms for Modeling Protein Structures and Motions
Abstract: With biomolecular structure recognized as central to understanding mechanisms in the cell, computational chemists and biophysicists have spent significant efforts on modeling structure and dynamics. While significant advances have been made, particularly in the design of sophisticated energetic models and molecular representations, such efforts are experiencing diminishing returns. One of the culprits is low exploration capability. The impasse has attracted AI researchers to offer adaptations of robot motion planning algorithms for modeling biomolecular structures and motions. This tutorial introduces students and researchers to robotics-inspired treatments and methodologies for understanding and elucidating the role of structure and dynamics in the function of biomolecules. The presentation is enhanced via an open-source software developed in the Shehu Computational Biology laboratory. The software allows researchers to integrate themselves in a new research domain and drive further research via plug-and-play capabilities. The hands-on approach in the the tutorial benefits both students and senior researchers keen to make contributions in computational structural biology.
Extracted statement — Material: Shehu Computational Biology laboratory
Research field: Oceanography
Title: Nitrous oxide emissions from the Arabian Sea
Abstract: Dissolved and atmospheric nitrous oxide (N2O) were measured on the legs 3 and 5 of the R/V Meteor cruise 32 in the Arabian Sea. A cruise track along 65°E was followed during both the intermonsoon (May 1995) and the southwest (SW) monsoon (July/August 1995) periods. During the second leg the coastal and open ocean upwelling regions off the Arabian Peninsula were also investigated. Mean N2O saturations for the oceanic regions of the Arabian Sea were in the range of 99–103% during the intermonsoon and 103–230% during the SW monsoon. Computed annual emissions of 0.8–1.5 Tg N2O for the Arabian Sea are considerably higher than previous estimates, indicating that the role of upwelling regions, such as the Arabian Sea, may be more important than previously assumed in global budgets of oceanic N2O emissions.
Extracted statement — Region of data collection: Arabian Sea
Research field: Oceanography
Title: Variabilities in the fluxes and annual emissions of nitrous oxide from the Arabian Sea
Abstract: Extensive measurements of nitrous oxide (N2O) have been made during April–May 1994 (intermonsoon), February–March 1995 (northeast monsoon), July–August 1995 and August 1996 (southwest monsoon) in the Arabian Sea. Low N2O supersaturations in the surface waters are observed during intermonsoon compared to those in northeast and southwest monsoons. Spatial distributions of supersaturations manifest the effects of larger mixing during winter cooling and wind‐driven upwelling during monsoon period off the Indian west coast. A net positive flux is observable during all the seasons, with no discernible differences from the open ocean to coastal regions. The average ocean‐to‐atmosphere fluxes of N2O are estimated, using wind speed dependent gas transfer velocity, to be of the order of 0.26, 0.003, and 0.51, and 0.78 pg (pico grams) cm−2 s−1 during northeast monsoon, intermonsoon, and southwest monsoon in 1995 and 1996, respectively. The lower range of annual emission of N2O is estimated to be 0.56–0.76 Tg N2O per year which constitutes 13–17% of the net global oceanic source. However, N2O emission from the Arabian Sea can be as high as 1.0 Tg N2O per year using different gas transfer models.
Extracted statement — Region of data collection: Arabian Sea
Research field: Oceanography
Title: A revised nitrogen budget for the Arabian Sea
Abstract: Despite its importance for the global oceanic nitrogen (N) cycle, considerable uncertainties exist about the N fluxes of the Arabian Sea. On the basis of our recent measurements during the German Arabian Sea Process Study as part of the Joint Global Ocean Flux Study (JGOFS) in 1995 and 1997, we present estimates of various N sources and sinks such as atmospheric dry and wet depositions of N aerosols, pelagic denitrification, nitrous oxide (N2O) emissions, and advective N input from the south. Additionally, we estimated the N burial in the deep sea and the sedimentary shelf denitrification. On the basis of our measurements and literature data, the N budget for the Arabian Sea was reassessed. It is dominated by the N loss due to denitrification, which is balanced by the advective input of N from the south. The role of N fixation in the Arabian Sea is still difficult to assess owing to the small database available; however, there are hints that it might be more important than previously thought. Atmospheric N depositions are important on a regional scale during the intermonsoon in the central Arabian Sea; however, they play only a minor role for the overall N cycling. Emissions of N2O and ammonia, deep‐sea N burial, and N inputs by rivers and marginal seas (i.e., Persian Gulf and Red Sea) are of minor importance. We found that the magnitude of the sedimentary denitrification at the shelf might be ∼17% of the total denitrification in the Arabian Sea, indicating that the shelf sediments might be of considerably greater importance for the N cycling in the Arabian Sea than previously thought. Sedimentary and pelagic denitrification together demand ∼6% of the estimated particulate organic nitrogen export flux from the photic zone. The main northward transport of N into the Arabian Sea occurs in the intermediate layers, indicating that the N cycle of the Arabian Sea might be sensitive to variations of the intermediate water circulation of the Indian Ocean.
Extracted statement — Region of data collection: Arabian Sea