Name
Title | ||
|---|---|---|
EnvMine: a text-mining system for the automatic extraction of contextual information. |
Abstract | ||
|---|---|---|
For 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.EnvMine 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.EnvMine 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1186/1471-2105-11-294 | BMC Bioinformatics |
Keywords | Field | DocType |
bayesian classifier,text mining,algorithms,bioinformatics,data mining,microarrays,bayes theorem,web pages | Units of measurement,Annotation,Information retrieval,Web page,Naive Bayes classifier,Computer science,Geographic coordinate system,Sampling (statistics),Bioinformatics,Schema (psychology),Bayes' theorem | Journal |
Volume | Issue | ISSN |
11 | 1 | 1471-2105 |
Citations | PageRank | References |
19 | 0.82 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Javier Tamames | 1 | 165 | 24.27 |
| Victor De Lorenzo | 2 | 32 | 4.12 |