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A deep learning approach to bilingual lexicon induction in the biomedical domain

Overview of attention for article published in BMC Bioinformatics, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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32 Mendeley
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Title
A deep learning approach to bilingual lexicon induction in the biomedical domain
Published in
BMC Bioinformatics, July 2018
DOI 10.1186/s12859-018-2245-8
Pubmed ID
Authors

Geert Heyman, Ivan Vulić, Marie-Francine Moens

Abstract

Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI as a classification problem and train a neural network composed of a combination of recurrent long short-term memory and deep feed-forward networks in order to obtain word-level and character-level representations. The results show that the word-level and character-level representations each improve state-of-the-art results for BLI and biomedical translation mining. The best results are obtained by exploiting the synergy between these word-level and character-level representations in the classification model. We evaluate the models both quantitatively and qualitatively. Translation of domain-specific biomedical terminology benefits from the character-level representations compared to relying solely on word-level representations. It is beneficial to take a deep learning approach and learn character-level representations rather than relying on handcrafted representations that are typically used. Our combined model captures the semantics at the word level while also taking into account that specialized terminology often originates from a common root form (e.g., from Greek or Latin).

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The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 19%
Student > Master 5 16%
Other 3 9%
Student > Ph. D. Student 3 9%
Student > Postgraduate 2 6%
Other 1 3%
Unknown 12 38%
Readers by discipline Count As %
Computer Science 7 22%
Social Sciences 2 6%
Linguistics 2 6%
Medicine and Dentistry 2 6%
Agricultural and Biological Sciences 2 6%
Other 5 16%
Unknown 12 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 13 February 2019.
All research outputs
#6,106,838
of 25,050,563 outputs
Outputs from BMC Bioinformatics
#2,040
of 7,641 outputs
Outputs of similar age
#95,752
of 332,429 outputs
Outputs of similar age from BMC Bioinformatics
#33
of 104 outputs
Altmetric has tracked 25,050,563 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,641 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 73% of its peers.
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 332,429 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 71% of its contemporaries.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.