Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Natural Language Processing
Title: BioCreAtIvE Task 1A: gene mention finding evaluation
Abstract: Abstract Background The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. "Finding mentions" is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI). Results 15 teams took part in task 1A. A number of teams achieved scores over 80% F-measure (balanced precision and recall). The teams that tried to use their task 1A systems to help on other BioCreAtIvE tasks reported mixed results. Conclusion The 80% plus F-measure results are good, but still somewhat lag the best scores achieved in some other domains such as newswire, due in part to the complexity and length of gene names, compared to person or organization names in newswire.
Extracted statement — Coarse-grained Entity type: Gene
Research field: Natural Language Processing
Title: BC4GO: a full-text corpus for the BioCreative IV GO task
Abstract: Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database groups. Owing to its manual nature, this task is considered one of the bottlenecks in literature curation. There have been many previous attempts at automatic identification of GO terms and supporting information from full text. However, few systems have delivered an accuracy that is comparable with humans. One recognized challenge in developing such systems is the lack of marked sentence-level evidence text that provides the basis for making GO annotations. We aim to create a corpus that includes the GO evidence text along with the three core elements of GO annotations: (i) a gene or gene product, (ii) a GO term and (iii) a GO evidence code. To ensure our results are consistent with real-life GO data, we recruited eight professional GO curators and asked them to follow their routine GO annotation protocols. Our annotators marked up more than 5000 text passages in 200 articles for 1356 distinct GO terms. For evidence sentence selection, the inter-annotator agreement (IAA) results are 9.3% (strict) and 42.7% (relaxed) in F1-measures. For GO term selection, the IAAs are 47% (strict) and 62.9% (hierarchical). Our corpus analysis further shows that abstracts contain ∼10% of relevant evidence sentences and 30% distinct GO terms, while the Results/Experiment section has nearly 60% relevant sentences and >70% GO terms. Further, of those evidence sentences found in abstracts, less than one-third contain enough experimental detail to fulfill the three core criteria of a GO annotation. This result demonstrates the need of using full-text articles for text mining GO annotations. Through its use at the BioCreative IV GO (BC4GO) task, we expect our corpus to become a valuable resource for the BioNLP research community. Database URL: http://www.biocreative.org/resources/corpora/bc-iv-go-task-corpus/.
Extracted statement — Coarse-grained Entity type: Gene
Research field: Natural Language Processing
Title: Web services-based text-mining demonstrates broad impacts for interoperability and process simplification
Abstract: The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/
Extracted statement — Coarse-grained Entity type: Gene
Research field: Natural Language Processing
Title: BioCreAtIvE Task 1A: gene mention finding evaluation
Abstract: Abstract Background The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. "Finding mentions" is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI). Results 15 teams took part in task 1A. A number of teams achieved scores over 80% F-measure (balanced precision and recall). The teams that tried to use their task 1A systems to help on other BioCreAtIvE tasks reported mixed results. Conclusion The 80% plus F-measure results are good, but still somewhat lag the best scores achieved in some other domains such as newswire, due in part to the complexity and length of gene names, compared to person or organization names in newswire.
Extracted statement — Concept types: Gene
Research field: Natural Language Processing
Title: BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID
Abstract: BioC is a simple XML format for text, annotations and relations, and was developed to achieve interoperability for biomedical text processing. Following the success of BioC in BioCreative IV, the BioCreative V BioC track addressed a collaborative task to build an assistant system for BioGRID curation. In this paper, we describe the framework of the collaborative BioC task and discuss our findings based on the user survey. This track consisted of eight subtasks including gene/protein/organism named entity recognition, protein–protein/genetic interaction passage identification and annotation visualization. Using BioC as their data-sharing and communication medium, nine teams, world-wide, participated and contributed either new methods or improvements of existing tools to address different subtasks of the BioC track. Results from different teams were shared in BioC and made available to other teams as they addressed different subtasks of the track. In the end, all submitted runs were merged using a machine learning classifier to produce an optimized output. The biocurator assistant system was evaluated by four BioGRID curators in terms of practical usability. The curators’ feedback was overall positive and highlighted the user-friendly design and the convenient gene/protein curation tool based on text mining. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-1-bioc/
Extracted statement — Concept types: gene
Research field: Natural Language Processing
Title: Overview of the CHEMDNER patents task
Abstract: A considerable effort has been made to extract biological and chemical entities, as well as their relationships, from the scientific literature, either manually through traditional literature curation or by using information extraction and text mining technologies. Medicinal chemistry patents contain a wealth of information, for instance to uncover potential biomarkers that might play a role in cancer treatment and prognosis. However, current biomedical annotation databases do not cover such information, partly due to limitations of publicly available biomedical patent mining software. As part of the BioCreative V CHEMDNER patents track, we present the results of the first named entity recognition (NER) assignment carried out to detect mentions of chemical compounds and genes/proteins in running patent text. More specifically, this task aimed to evaluate the performance of automatic name recognition strategies capable of isolating chemical names and gene and gene product mentions from surrounding text within patent titles and abstracts. A total of 22 unique teams submitted results for at least one of the three CHEMDNER subtasks. The first subtask, called the CEMP (chemical entity mention in patents) task, focused on the detection of chemical named entity mentions in patents, requesting teams to return the start and end indices corresponding to all the chemical entities found in a given record. A total of 21 teams submitted 93 runs, for this subtask. The top performing team reached an f-measure of 0.89 with a precision of 0.87 and a recall of 0.91. The CPD (chemical passage detection) task required the classification of patent titles and abstracts whether they do or do not contain chemical compound mentions. Nine teams returned predictions for this task (40 runs). The top run in terms of Matthew’s correlation coefficient (MCC) had a score of 0.88, the highest sensitivity ? Corresponding author
Extracted statement — Concept types: gene
Research field: Natural Language Processing
Title: An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks
Abstract: The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. Based on the corpus, the AGAC track of the BioNLP Open Shared Tasks 2019 was organized, to facilitate cross-disciplinary collaboration across BioNLP and Pharmacoinformatics communities, for drug repurposing. The AGAC track consists of three subtasks: 1) named entity recognition, 2) thematic relation extraction, and 3) loss of function (LOF) / gain of function (GOF) topic classification. The AGAC track was participated by five teams, of which the performance are compared and analyzed. The the results revealed a substantial room for improvement in the design of the task, which we analyzed in terms of “imbalanced data”, “selective annotation” and “latent topic annotation”.
Extracted statement — Concept types: Gene
Research field: Natural Language Processing
Title: Overview of the Infectious Diseases (ID) task of BioNLP Shared Task 2011
Abstract: This paper presents the preparation, resources, results and analysis of the Infectious Diseases (ID) information extraction task, a main task of the BioNLP Shared Task 2011. The ID task represents an application and extension of the BioNLP'09 shared task event extraction approach to full papers on infectious diseases. Seven teams submitted final results to the task, with the highest-performing system achieving 56% F-score in the full task, comparable to state-of-the-art performance in the established BioNLP'09 task. The results indicate that event extraction methods generalize well to new domains and full-text publications and are applicable to the extraction of events relevant to the molecular mechanisms of infectious diseases.
Extracted statement — Entity types: Gene
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: Gene
Research field: Natural Language Processing
Title: An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks
Abstract: The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. Based on the corpus, the AGAC track of the BioNLP Open Shared Tasks 2019 was organized, to facilitate cross-disciplinary collaboration across BioNLP and Pharmacoinformatics communities, for drug repurposing. The AGAC track consists of three subtasks: 1) named entity recognition, 2) thematic relation extraction, and 3) loss of function (LOF) / gain of function (GOF) topic classification. The AGAC track was participated by five teams, of which the performance are compared and analyzed. The the results revealed a substantial room for improvement in the design of the task, which we analyzed in terms of “imbalanced data”, “selective annotation” and “latent topic annotation”.
Extracted statement — Entity types: Gene
Research field: Natural Language Processing
Title: BioNLP Shared Task 2013 – An overview of the Genic Regulation Network Task
Abstract: The goal of the Genic Regulation Network task (GRN) is to extract a regulation network that links and integrates a v a r i e y o f m o l e a interactions between genes and proteins of the well-studied model bacterium Bacillus subtilis. It is an extension of the BI task of BioNLP-ST’11. The corpus is composed of sentences selected from publicly available PubMed scientific
Extracted statement — Entity types: Gene
Research field: Natural Language Processing
Title: Overview of the Cancer Genetics (CG) task of BioNLP Shared Task 2013
Abstract: We present the design, preparation, results and analysis of the Cancer Genetics (CG) event extraction task, a main task of the BioNLP Shared Task (ST) 2013. The CG task is an information extraction task targeting the recognition of events in text, represented as structured n-ary associations of given physical entities. In addition to addressing the cancer domain, the CG task is differentiated from previous event extraction tasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple levels of biological organization, ranging from the molecular through the cellular and organ levels up to whole organisms. Final test set submissions were accepted from six teams. The highest-performing system achieved an Fscore of 55.4%. This level of performance is broadly comparable with the state of the art for established molecular-level extraction tasks, demonstrating that event extraction resources and methods generalize well to higher levels of biological organization and are applicable to the analysis of scientific texts on cancer. The CG task continues as an open challenge to all interested parties, with tools and resources available from http://2013. bionlp-st.org/.
Extracted statement — Event / Relation Types: General
Research field: Natural Language Processing
Title: Overview of the Cancer Genetics (CG) task of BioNLP Shared Task 2013
Abstract: We present the design, preparation, results and analysis of the Cancer Genetics (CG) event extraction task, a main task of the BioNLP Shared Task (ST) 2013. The CG task is an information extraction task targeting the recognition of events in text, represented as structured n-ary associations of given physical entities. In addition to addressing the cancer domain, the CG task is differentiated from previous event extraction tasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple levels of biological organization, ranging from the molecular through the cellular and organ levels up to whole organisms. Final test set submissions were accepted from six teams. The highest-performing system achieved an Fscore of 55.4%. This level of performance is broadly comparable with the state of the art for established molecular-level extraction tasks, demonstrating that event extraction resources and methods generalize well to higher levels of biological organization and are applicable to the analysis of scientific texts on cancer. The CG task continues as an open challenge to all interested parties, with tools and resources available from http://2013. bionlp-st.org/.
Extracted statement — Event types: General
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: Habitat
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: Habitat
Research field: Natural Language Processing
Title: HeidelTime: High Quality Rule-Based Extraction and Normalization of Temporal Expressions
Abstract: In this paper, we describe HeidelTime, a system for the extraction and normalization of temporal expressions. HeidelTime is a rule-based system mainly using regular expression patterns for the extraction of temporal expressions and knowledge resources as well as linguistic clues for their normalization. In the TempEval-2 challenge, HeidelTime achieved the highest F-Score (86%) for the extraction and the best results in assigning the correct value attribute, i.e., in understanding the semantics of the temporal expressions.
Extracted statement — Tool name: HeidelTime
Research field: Natural Language Processing
Title: Named Entity Recognition for Astronomy Literature
Abstract: We present a system for named entity recognition (ner) in astronomy journal articles. We have developed this system on a ne corpus comprising approximately 200,000 words of text from astronomy articles. These have been manually annotated with ∼40 entity types of interest to astronomers. We report on the challenges involved in extracting the corpus, defining entity classes and annotating scientific text. We investigate which features of an existing state-of-the-art Maximum Entropy approach perform well on astronomy text. Our system achieves an F-score of 87.8%.
Extracted statement — Fine-grained Entity types: ion
Research field: Natural Language Processing
Title: Transforming Wikipedia into Named Entity Training Data
Abstract: Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated text. We transform Wikipedia’s links into named entity annotations by classifying the target articles into common entity types (e.g. person, organisation and location). Comparing to MUC, CONLL and BBN corpora, Wikipedia generally performs better than other cross-corpus train/test pairs.
Extracted statement — Coarse-grained Entity types: Location
Research field: Natural Language Processing
Title: SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Abstract: We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.
Extracted statement — Concept types: Material