Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Natural Language Processing
Title: WEXEA: Wikipedia EXhaustive Entity Annotation
Abstract: Building predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated text. Wikipedia is often used as a corpus for these tasks where the annotation is a named entity linked by a hyperlink to its article. However, editors on Wikipedia are only expected to link these mentions in order to help the reader to understand the content, but are discouraged from adding links that do not add any benefit for understanding an article. Therefore, many mentions of popular entities (such as countries or popular events in history), or previously linked articles, as well as the article’s entity itself, are not linked. In this paper, we discuss WEXEA, a Wikipedia EXhaustive Entity Annotation system, to create a text corpus based on Wikipedia with exhaustive annotations of entity mentions, i.e. linking all mentions of entities to their corresponding articles. This results in a huge potential for additional annotations that can be used for downstream NLP tasks, such as Relation Extraction. We show that our annotations are useful for creating distantly supervised datasets for this task. Furthermore, we publish all code necessary to derive a corpus from a raw Wikipedia dump, so that it can be reproduced by everyone.
Extracted statement — Dataset name: WEXEA
Research field: Natural Language Processing
Title: Coreference Resolution in Biomedical Texts: a Machine Learning Approach
Abstract: Motivation: Coreference resolution, the process of identifying different mentions of an entity, is a very important component in a text-mining system. Compared with the work in news articles, the existing study of coreference resolution in biomedical texts is quite preliminary by only focusing on specific types of anaphors like pronouns or definite noun phrases, using heuristic methods, and running on small data sets. Therefore, there is a need for an in-depth exploration of this task in the biomedical domain. Results: In this article, we presented a learning-based approach to coreference resolution in the biomedical domain. We made three contributions in our study. Firstly, we annotated a large scale coreference corpus, MedCo, which consists of 1,999 medline abstracts in the GENIA data set. Secondly, we proposed a detailed framework for the coreference resolution task, in which we augmented the traditional learning model by incorporating non-anaphors into training. Lastly, we explored various sources of knowledge for coreference resolution, particularly, those that can deal with the complexity of biomedical texts. The evaluation on the MedCo corpus showed promising results. Our coreference resolution system achieved a high precision of 85.2% with a reasonable recall of 65.3%, obtaining an F-measure of 73.9%. The results also suggested that our augmented learning model significantly boosted precision (up to 24.0%) without much loss in recall (less than 5%), and brought a gain of over 8% in F-measure.
Extracted statement — Dataset name: MedCo
Research field: Natural Language Processing
Title: Overview of the NLM-Chem BioCreative VII track: Full-text Chemical Identification and Indexing in PubMed articles
Abstract: The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature. Chemical names are one of the most searched biomedical entities in PubMed and – as highlighted during the COVID-19 pandemic – their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We organized the BioCreative NLM-Chem track to call for a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: 1) Chemical Identification task, and 2) Chemical Indexing prediction task. For the Chemical Identification task, participants were expected to predict with high accuracy all chemicals mentioned in recently published full-text articles, both span (i.e., named entity recognition) and normalization (i.e., entity linking) using MeSH. For the Chemical Indexing task, participants identified which chemicals should be indexed as topics for the article's topic terms in the NLM article and indexing, i.e., appear in the listing of MeSH terms for the document. This manuscript summarizes the BioCreative NLM-Chem track. We received a total of 88 submissions in total from 17 teams worldwide. The highest performance achieved for the Chemical Identification task was 0.8672 f-score (0.8759 precision, 0.8587 recall) for strict NER performance and 0.8136 f-score (0.8621 precision, 0.7702 recall) for strict normalization performance. The highest performance achieved for the Chemical Indexing task was 0.4825 f-score (0.4397 precision, 0.5344 recall). The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. This community challenge demonstrated 1) the current substantial achievements in deep learning technologies can be utilized to further improve automated prediction accuracy, and 2) the Chemical Indexing task is substantially more challenging. We look forward to further development of biomedical text mining methods to respond to the rapid growth of biomedical literature. Keywords— biomedical text mining; natural language processing; artificial intelligence; machine learning; deep learning; text mining; chemical entity recognition; chemical indexing
Extracted statement — Ontology used: MeSH
Research field: Natural Language Processing
Title: SPECTER: Document-level Representation Learning using Citation-informed Transformers
Abstract: Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.
Extracted statement — Material: SciDocs
Research field: Natural Language Processing
Title: SPECTER: Document-level Representation Learning using Citation-informed Transformers
Abstract: Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.
Extracted statement — On evaluation dataset: SciDocs
Research field: Natural Language Processing
Title: Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Abstract: We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
Extracted statement — Dataset name: SciERC
Research field: Natural Language Processing
Title: Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Abstract: We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
Extracted statement — model: SciIE
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 — Dataset name: SciREX
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 — Ontologies used: SNOMED-CT
Research field: Natural Language Processing
Title: Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
Abstract: In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.
Extracted statement — Dataset name: ENG-SPA
Research field: Natural Language Processing
Title: An empirical evaluation of resources for the identification of diseases and adverse effects in biomedical literature
Abstract: The mentions of human health perturbations such as the diseases and adverse effects denote a special entity class in the biomedical literature. They help in understanding the underlying risk factors and develop a preventive rationale. The recognition of these named entities in texts through dictionary-based approaches relies on the availability of appropriate terminological resources. Although few resources are publicly available, not all are suitable for the text mining needs. Therefore, this work provides an overview of the well known resources with respect to human diseases and adverse effects such as the MeSH, MedDRA, ICD-10, SNOMED CT, and UMLS. Individual dictionaries are generated from these resources and their performance in recognizing the named entities is evaluated over a manually annotated corpus. In addition, the steps for curating the dictionaries, rule-based acronym disambiguation and their impact on the dictionary performance is discussed. The results show that the MedDRA and UMLS achieve the best recall. Besides this, MedDRA provides an additional benefit of achieving a higher precision. The combination of search results of all the dictionaries achieve a considerably high recall. The corpus is available on http://www.scai.fraunhofer.de/disease-ae-corpus.html
Extracted statement — Other resources: ICD-10
Research field: Natural Language Processing
Title: Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
Abstract: In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.
Extracted statement — Dataset name: MSA-EGY
Research field: Organic Chemistry
Title: Atmospheric Hydrodeoxygenation of Guaiacol over Alumina-, Zirconia-, and Silica-Supported Nickel Phosphide Catalysts
Abstract: This study investigated atmospheric hydrodeoxygenation (HDO) of guaiacol over Ni2P-supported catalysts. Alumina, zirconia, and silica served as the supports of Ni2P catalysts. The physicochemical properties of these catalysts were surveyed by N2 physisorption, X-ray diffraction (XRD), CO chemisorption, H2 temperature-programmed reduction (H2-TPR), H2 temperature-programmed desorption (H2-TPD), and NH3 temperature-programmed desorption (NH3-TPD). The catalytic performance of these catalysts was tested in a continuous fixed-bed system. This paper proposes a plausible network of atmospheric guaiacol HDO, containing demethoxylation (DMO), demethylation (DME), direct deoxygenation (DDO), hydrogenation (HYD), transalkylation, and methylation. Pseudo-first-order kinetics analysis shows that the intrinsic activity declined in the following order: Ni2P/ZrO2 > Ni2P/Al2O3 > Ni2P/SiO2. Product selectivity at zero guaiacol conversion indicates that Ni2P/SiO2 promotes DMO and DDO routes, whereas Ni2P/ZrO2 and Ni2P/Al2O...
Extracted statement — catalyst: Ni2P/SiO2
Research field: Physical Chemistry
Title: Continuous Symmetry Breaking Induced by Ion Pairing Effect in Heptamethine Cyanine Dyes: Beyond the Cyanine Limit
Abstract: The association of heptamethine cyanine cation 1(+) with various counterions A (A = Br(-), I(-), PF(6)(-), SbF(6)(-), B(C(6)F(5))(4)(-), TRISPHAT) was realized. The six different ion pairs have been characterized by X-ray diffraction, and their absorption properties were studied in polar (DCM) and apolar (toluene) solvents. A small, hard anion (Br(-)) is able to strongly polarize the polymethine chain, resulting in the stabilization of an asymmetric dipolar-like structure in the crystal and in nondissociating solvents. On the contrary, in more polar solvents or when it is associated with a bulky soft anion (TRISPHAT or B(C(6)F(5))(4)(-)), the same cyanine dye adopts preferentially the ideal polymethine state. The solid-state and solution absorption properties of heptamethine dyes are therefore strongly correlated to the nature of the counterion.
Extracted statement — Counterion: TRISPHAT
Research field: Physical Chemistry
Title: Continuous Symmetry Breaking Induced by Ion Pairing Effect in Heptamethine Cyanine Dyes: Beyond the Cyanine Limit
Abstract: The association of heptamethine cyanine cation 1(+) with various counterions A (A = Br(-), I(-), PF(6)(-), SbF(6)(-), B(C(6)F(5))(4)(-), TRISPHAT) was realized. The six different ion pairs have been characterized by X-ray diffraction, and their absorption properties were studied in polar (DCM) and apolar (toluene) solvents. A small, hard anion (Br(-)) is able to strongly polarize the polymethine chain, resulting in the stabilization of an asymmetric dipolar-like structure in the crystal and in nondissociating solvents. On the contrary, in more polar solvents or when it is associated with a bulky soft anion (TRISPHAT or B(C(6)F(5))(4)(-)), the same cyanine dye adopts preferentially the ideal polymethine state. The solid-state and solution absorption properties of heptamethine dyes are therefore strongly correlated to the nature of the counterion.
Extracted statement — BLA evaluation method: X-Ray
Research field: Planetary Sciences
Title: Compositional stratigraphy of clay-bearing layered deposits at Mawrth Vallis, Mars: STRATIGRAPHY OF CLAY-BEARING DEPOSITS ON MARS
Abstract: Phyllosilicates have previously been detected in layered outcrops in and around the Martian outflow channel Mawrth Vallis. CRISM spectra of these outcrops exhibit features diagnostic of kaolinite, montmorillonite, and Fe/Mg‐rich smectites, along with crystalline ferric oxide minerals such as hematite. These minerals occur in distinct stratigraphic horizons, implying changing environmental conditions and/or a variable sediment source for these layered deposits. Similar stratigraphic sequences occur on both sides of the outflow channel and on its floor, with Al‐clay‐bearing layers typically overlying Fe/Mg‐clay‐bearing layers. This pattern, combined with layer geometries measured using topographic data from HiRISE and HRSC, suggests that the Al‐clay‐bearing horizons at Mawrth Vallis postdate the outflow channel and may represent a later sedimentary or altered pyroclastic deposit that drapes the topography.
Extracted statement — Supplimentary Information:  HRSC
Research field: Planetary Sciences
Title: Raman spectroscopy for mineral identification and quantification for in situ planetary surface analysis: A point count method
Abstract: Quantification of mineral proportions in rocks and soils by Raman spectroscopy on a planetary surface is best done by taking many narrow-beam spectra from different locations on the rock or soil, with each spectrum yielding peaks from only one or two minerals. The proportion of each mineral in the rock or soil can then be determined from the fraction of the spectra that contain its peaks, in analogy with the standard petrographic technique of point counting. The method can also be used for nondestructive laboratory characterization of rock samples. Although Raman peaks for different minerals seldom overlap each other, it is impractical to obtain proportions of constituent minerals by Raman spectroscopy through analysis of peak intensities in a spectrum obtained by broad-beam sensing of a representative area of the target material. That is because the Raman signal strength produced by a mineral in a rock or soil is not related in a simple way through the Raman scattering cross section of that mineral to its proportion in the rock, and the signal-to-noise ratio of a Raman spectrum is poor when a sample is stimulated by a low-power laser beam of broad diameter. Results obtained by the Raman point-count method are demonstrated for a lunar thin section (14161,7062) and a rock fragment (15273,7039). Major minerals (plagioclase and pyroxene), minor minerals (cristobalite and K-feldspar), and accessory minerals (whitlockite, apatite, and baddeleyite) were easily identified. Identification of the rock types, KREEP basalt or melt rock, from the 100-location spectra was straightforward.
Extracted statement — Samples: 141,617,062