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Recognizing chemicals in patents: a comparative analysis

Overview of attention for article published in Journal of Cheminformatics, October 2016
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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6 tweeters

Citations

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10 Dimensions

Readers on

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31 Mendeley
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Title
Recognizing chemicals in patents: a comparative analysis
Published in
Journal of Cheminformatics, October 2016
DOI 10.1186/s13321-016-0172-0
Pubmed ID
Authors

Maryam Habibi, David Luis Wiegandt, Florian Schmedding, Ulf Leser

Abstract

Recently, methods for Chemical Named Entity Recognition (NER) have gained substantial interest, driven by the need for automatically analyzing todays ever growing collections of biomedical text. Chemical NER for patents is particularly essential due to the high economic importance of pharmaceutical findings. However, NER on patents has essentially been neglected by the research community for long, mostly because of the lack of enough annotated corpora. A recent international competition specifically targeted this task, but evaluated tools only on gold standard patent abstracts instead of full patents; furthermore, results from such competitions are often difficult to extrapolate to real-life settings due to the relatively high homogeneity of training and test data. Here, we evaluate the two state-of-the-art chemical NER tools, tmChem and ChemSpot, on four different annotated patent corpora, two of which consist of full texts. We study the overall performance of the tools, compare their results at the instance level, report on high-recall and high-precision ensembles, and perform cross-corpus and intra-corpus evaluations. Our findings indicate that full patents are considerably harder to analyze than patent abstracts and clearly confirm the common wisdom that using the same text genre (patent vs. scientific) and text type (abstract vs. full text) for training and testing is a pre-requisite for achieving high quality text mining results.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Netherlands 1 3%
Denmark 1 3%
Germany 1 3%
Unknown 27 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 32%
Student > Master 7 23%
Student > Ph. D. Student 4 13%
Other 3 10%
Librarian 1 3%
Other 2 6%
Unknown 4 13%
Readers by discipline Count As %
Computer Science 10 32%
Chemistry 5 16%
Pharmacology, Toxicology and Pharmaceutical Science 3 10%
Agricultural and Biological Sciences 3 10%
Business, Management and Accounting 1 3%
Other 6 19%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 November 2016.
All research outputs
#3,155,756
of 11,350,788 outputs
Outputs from Journal of Cheminformatics
#267
of 444 outputs
Outputs of similar age
#88,902
of 255,523 outputs
Outputs of similar age from Journal of Cheminformatics
#13
of 25 outputs
Altmetric has tracked 11,350,788 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 444 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.7. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 255,523 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.