Dataset Information

Using typed dependencies to study and recognise conceptualisation zones in biomedical literature.

ABSTRACT: In the biomedical domain, authors publish their experiments and findings using a quasi-standard coarse-grained discourse structure, which starts with an introduction that sets up the motivation, continues with a description of the materials and methods, and concludes with results and discussions. Over the course of the years, there has been a fair amount of research done in the area of scientific discourse analysis, with a focus on performing automatic recognition of scientific artefacts/conceptualisation zones from the raw content of scientific publications. Most of the existing approaches use Machine Learning techniques to perform classification based on features that rely on the shallow structure of the sentence tokens, or sentences as a whole, in addition to corpus-driven statistics. In this article, we investigate the role carried by the deep (dependency) structure of the sentences in describing their rhetorical nature. Using association rule mining techniques, we study the presence of dependency structure patterns in the context of a given rhetorical type, the use of these patterns in exploring differences in structure between the rhetorical types, and their ability to discriminate between the different rhetorical types. Our final goal is to provide a series of insights that can be used to complement existing classification approaches. Experimental results show that, in particular in the context of a fine-grained multi-class classification context, the association rules emerged from the dependency structure are not able to produce uniform classification results. However, they can be used to derive discriminative pair-wise classification mechanisms, in particular for some of the most ambiguous types.

SUBMITTER: Groza T

PROVIDER: S-EPMC3832541 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Using typed dependencies to study and recognise conceptualisation zones in biomedical literature.

Groza Tudor T

PloS one 20131118 11

In the biomedical domain, authors publish their experiments and findings using a quasi-standard coarse-grained discourse structure, which starts with an introduction that sets up the motivation, continues with a description of the materials and methods, and concludes with results and discussions. Over the course of the years, there has been a fair amount of research done in the area of scientific discourse analysis, with a focus on performing automatic recognition of scientific artefacts/concept ...[more]

PMID: 24260252

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Project description:BackgroundValuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng.MethodsThe system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver.ResultsFrom the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect.ConclusionThe Bio-DTM model not only discovers what ginseng's research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies.

Project description:BACKGROUND:With the substantial growth in the biomedical research literature, a larger number of claims are published daily, some of which seemingly disagree with or contradict prior claims on the same topics. Resolving such contradictions is critical to advancing our understanding of human disease and developing effective treatments. Automated text analysis techniques can facilitate such analysis by extracting claims from the literature, flagging those that are potentially contradictory, and identifying any study characteristics that may explain such contradictions. METHODS:Using SemMedDB, our own PubMed-scale repository of semantic predications (subject-relation-object triples), we identified apparent contradictions in the biomedical research literature and developed a categorization of contextual characteristics that explain such contradictions. Clinically relevant semantic predications relating to 20 diseases and involving opposing predicate pairs (e.g., an intervention treats or causes a disease) were retrieved from SemMedDB. After addressing inference, uncertainty, generic concepts, and NLP errors through automatic and manual filtering steps, a set of apparent contradictions were identified and characterized. RESULTS:We retrieved 117,676 predication instances from 62,360 PubMed abstracts (Jan 1980-Dec 2016). From these instances, automatic filtering steps generated 2236 candidate contradictory pairs. Through manual analysis, we determined that 58 of these pairs (2.6%) were apparent contradictions. We identified five main categories of contextual characteristics that explain these contradictions: (a) internal to the patient, (b) external to the patient, (c) endogenous/exogenous, (d) known controversy, and (e) contradictions in literature. Categories (a) and (b) were subcategorized further (e.g., species, dosage) and accounted for the bulk of the contradictory information. CONCLUSIONS:Semantic predications, by accounting for lexical variability, and SemMedDB, owing to its literature scale, can support identification and elucidation of potentially contradictory claims across the biomedical domain. Further filtering and classification steps are needed to distinguish among them the true contradictory claims. The ability to detect contradictions automatically can facilitate important biomedical knowledge management tasks, such as tracking and verifying scientific claims, summarizing research on a given topic, identifying knowledge gaps, and assessing evidence for systematic reviews, with potential benefits to the scientific community. Future work will focus on automating these steps for fully automatic recognition of contradictions from the biomedical research literature.

Project description:Motivation:In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models. Results:We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research. Availability and implementation:The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material. Contact:sophia.ananiadou@manchester.ac.uk. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Using typed dependencies to study and recognise conceptualisation zones in biomedical literature.

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Using typed dependencies to study and recognise conceptualisation zones in biomedical literature.

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