Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Computational Engineering
Title: Predicting Infections Using Computational Intelligence – A Systematic Review
Abstract: Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.
Extracted statement — Has result: Machine learning techniques
Research field: Computational Engineering
Title: Learning to Generate Wikipedia Summaries for Underserved Languages
            from Wikidata
Abstract: While Wikipedia exists in 287 languages, its content is unevenly distributed among them. In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata. To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples. We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.
Extracted statement — Data: open domain Wikipedia summaries
Research field: Computational Engineering
Title: CATS: Characterizing automation of Twitter spammers
Abstract: Twitter, with its rising popularity as a micro-blogging website, has inevitably attracted the attention of spammers. Spammers use myriad of techniques to evade security mechanisms and post spam messages, which are either unwelcome advertisements for the victim or lure victims in to clicking malicious URLs embedded in spam tweets. In this paper, we propose several novel features capable of distinguishing spam accounts from legitimate accounts. The features analyze the behavioral and content entropy, bait-techniques, and profile vectors characterizing spammers, which are then fed into supervised learning algorithms to generate models for our tool, CATS. Using our system on two real-world Twitter data sets, we observe a 96% detection rate with about 0.8% false positive rate beating state of the art detection approach. Our analysis reveals detection of more than 90% of spammers with less than five tweets and about half of the spammers detected with only a single tweet. Our feature computation has low latency and resource requirement making fast detection feasible. Additionally, we cluster the unknown spammers to identify and understand the prevalent spam campaigns on Twitter.
Extracted statement — Method: Supervised learning algorithms
Research field: Computational Engineering
Title: Predicting Infections Using Computational Intelligence – A Systematic Review
Abstract: Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.
Extracted statement — Has method: Systematic Literature Review
Research field: Computational Engineering
Title: Learning to Generate Wikipedia Summaries for Underserved Languages
            from Wikidata
Abstract: While Wikipedia exists in 287 languages, its content is unevenly distributed among them. In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata. To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples. We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.
Extracted statement — Material: underserved languages
Research field: Computational Linguistics
Title: Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Abstract: Knowledge base question answering (KBQA) is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training datasets. In this work, we propose Neuro-Symbolic Question Answering (NSQA), a modular KBQA system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question understanding; (2) a simple yet effective graph transformation approach to convert AMR parses into candidate logical queries that are aligned to the KB; (3) a pipeline-based approach which integrates multiple, reusable modules that are trained specifically for their individual tasks (semantic parser, entity and relationship linkers, and neuro-symbolic reasoner) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia (QALD-9 and LC-QuAD 1.0). Furthermore, our analysis emphasizes that AMR is a powerful tool for KBQA systems.
Extracted statement — Techniques/Methods: Abstract Meaning Representation
Research field: Computational Linguistics
Title: The ACL Anthology Network
Abstract: We introduce the ACL Anthology Network (AAN), a manually curated networked database of citations, collaborations, and summaries in the field of Computational Linguistics. We also present a number of statistics about the network including the most cited authors, the most central collaborators, as well as network statistics about the paper citation, author citation, and author collaboration networks.
Extracted statement — consists: Author collaboration network
Research field: Computational Linguistics
Title: Construction of an annotated corpus to support biomedical information extraction
Abstract: Abstract Background Information Extraction (IE) is a component of text mining that facilitates knowledge discovery by automatically locating instances of interesting biomedical events from huge document collections. As events are usually centred on verbs and nominalised verbs, understanding the syntactic and semantic behaviour of these words is highly important. Corpora annotated with information concerning this behaviour can constitute a valuable resource in the training of IE components and resources. Results We have defined a new scheme for annotating sentence-bound gene regulation events, centred on both verbs and nominalised verbs. For each event instance, all participants ( arguments ) in the same sentence are identified and assigned a semantic role from a rich set of 13 roles tailored to biomedical research articles, together with a biological concept type linked to the Gene Regulation Ontology. To our knowledge, our scheme is unique within the biomedical field in terms of the range of event arguments identified. Using the scheme, we have created the Gene Regulation Event Corpus (GREC), consisting of 240 MEDLINE abstracts, in which events relating to gene regulation and expression have been annotated by biologists. A novel method of evaluating various different facets of the annotation task showed that average inter-annotator agreement rates fall within the range of 66% - 90%. Conclusion The GREC is a unique resource within the biomedical field, in that it annotates not only core relationships between entities, but also a range of other important details about these relationships, e.g., location, temporal, manner and environmental conditions. As such, it is specifically designed to support bio-specific tool and resource development. It has already been used to acquire semantic frames for inclusion within the BioLexicon (a lexical, terminological resource to aid biomedical text mining). Initial experiments have also shown that the corpus may viably be used to train IE components, such as semantic role labellers. The corpus and annotation guidelines are freely available for academic purposes.
Extracted statement — Dataset name: Gene Regulation Event Corpus
Research field: Computational Linguistics
Title: Construction of an annotated corpus to support biomedical information extraction
Abstract: Abstract Background Information Extraction (IE) is a component of text mining that facilitates knowledge discovery by automatically locating instances of interesting biomedical events from huge document collections. As events are usually centred on verbs and nominalised verbs, understanding the syntactic and semantic behaviour of these words is highly important. Corpora annotated with information concerning this behaviour can constitute a valuable resource in the training of IE components and resources. Results We have defined a new scheme for annotating sentence-bound gene regulation events, centred on both verbs and nominalised verbs. For each event instance, all participants ( arguments ) in the same sentence are identified and assigned a semantic role from a rich set of 13 roles tailored to biomedical research articles, together with a biological concept type linked to the Gene Regulation Ontology. To our knowledge, our scheme is unique within the biomedical field in terms of the range of event arguments identified. Using the scheme, we have created the Gene Regulation Event Corpus (GREC), consisting of 240 MEDLINE abstracts, in which events relating to gene regulation and expression have been annotated by biologists. A novel method of evaluating various different facets of the annotation task showed that average inter-annotator agreement rates fall within the range of 66% - 90%. Conclusion The GREC is a unique resource within the biomedical field, in that it annotates not only core relationships between entities, but also a range of other important details about these relationships, e.g., location, temporal, manner and environmental conditions. As such, it is specifically designed to support bio-specific tool and resource development. It has already been used to acquire semantic frames for inclusion within the BioLexicon (a lexical, terminological resource to aid biomedical text mining). Initial experiments have also shown that the corpus may viably be used to train IE components, such as semantic role labellers. The corpus and annotation guidelines are freely available for academic purposes.
Extracted statement — Other resources: Gene Regulation Ontology
Research field: Computational Linguistics
Title: GENIA corpus--a semantically annotated corpus for bio-textmining
Abstract: MOTIVATION Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining. RESULTS GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400,000 words and almost 100,000 annotations for biological terms.
Extracted statement — Dataset name: GENIA corpus
Research field: Computational Linguistics
Title: Syntax Annotation for the GENIA Corpus
Abstract: Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.
Extracted statement — Dataset name: GENIA corpus
Research field: Computational Linguistics
Title: The GENIA corpus: an annotated research abstract corpus in molecular biology domain
Abstract: With the information overload in genome-related field, there is an increasing need for natural language processing technology to extract information from literature and various attempts of information extraction using NLP has been being made. We are developing the necessary resources including domain ontology and annotated corpus from research abstracts in MEDLINE database (GENIA corpus). We are building the ontology and the corpus simultaneously, using each other. In this paper we report on our new corpus, its ontological basis, annotation scheme, and statistics of annotated objects. We also describe the tools used for corpus annotation and management.
Extracted statement — Dataset name: GENIA corpus
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Dataset name: ITI TXM corpora
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Other resources: NCBI Taxonomy
Research field: Computational Linguistics
Title: Syntax Annotation for the GENIA Corpus
Abstract: Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.
Extracted statement — Annotation scheme: Penn Treebank II (PTB) scheme
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Data domains: protein-protein interactions
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Data domains: tissue expression
Research field: Computational Linguistics
Title: BioNLP Shared Task 2011 - Bacteria Biotope
Abstract: This paper presents the Bacteria Biotope task as part of the BioNLP Shared Tasks 2011. The Bacteria Biotope task aims at extracting the location of bacteria from scientific Web pages. Bacteria location is a crucial knowledge in biology for phenotype studies. The paper details the corpus specification, the evaluation metrics, summarizes and discusses the participant results.
Extracted statement — Data coverage: Web pages
Research field: Computer and Systems Architecture
Title: Efficient synthesis of physically valid human motion
Abstract: Optimization is a promising way to generate new animations from a minimal amount of input data. Physically based optimization techniques, however, are difficult to scale to complex animated characters, in part because evaluating and differentiating physical quantities becomes prohibitively slow. Traditional approaches often require optimizing or constraining parameters involving joint torques; obtaining first derivatives for these parameters is generally an O(D2) process, where D is the number of degrees of freedom of the character. In this paper, we describe a set of objective functions and constraints that lead to linear time analytical first derivatives. The surprising finding is that this set includes constraints on physical validity, such as ground contact constraints. Considering only constraints and objective functions that lead to linear time first derivatives results in fast per-iteration computation times and an optimization problem that appears to scale well to more complex characters. We show that qualities such as squash-and-stretch that are expected from physically based optimization result from our approach. Our animation system is particularly useful for synthesizing highly dynamic motions, and we show examples of swinging and leaping motions for characters having from 7 to 22 degrees of freedom.
Extracted statement — has parameters: Joint torques