Evidence map›Paper›PMID 20515448›Full record

ArticleBMC bioinformatics2010

EnvMine: a text-mining system for the automatic extraction of contextual information.

Javier Tamames, Victor de Lorenzo

Abstract read
In one paragraph

Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019
    Article
  3. Article
  4. Named entity linking of geospatial and host metadata in GenBank for advancing biomedical research.Database : the journal of biological databases and curation · 2017
    Article
  5. A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records.Journal of the American Medical Informatics Association : JAMIA · 2016
    Article
  6. Article
  7. BioNLP Shared Task--The Bacteria Track.BMC bioinformatics · 2012
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Javier TamamesCentro Nacional de Biotecnología (CNB), CSIC, C/Darwin 3, 28049 Madrid, Spain. jtamames@cnb.csic.es
Victor de Lorenzo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles). So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations) from textual sources of any kind.

resultsEnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved) of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings.Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude), thus allowing the calculation of distance between the individual locations.

conclusionEnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical variables of sampling sites, thus facilitating the performance of ecological analyses. EnvMine can also help in the development of standards for the annotation of environmental features.

Indexed as

Bayes TheoremDatabases, FactualData MiningInformation Storage and RetrievalPattern Recognition, AutomatedPeriodicals as Topic

Identifiers

PMID20515448
PMCPMC2901371

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.