Evaluating Sparse Interpretable Word Embeddings for Biomedical Domain Article Swipe
Mohammad Amin Samadi
,
Mohammad Sadegh Akhondzadeh
,
Sayed Jalal Zahabi
,
Mohammad Hossein Manshaei
,
Zeinab Maleki
,
Payman Adibi
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.05114
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.05114
Word embeddings have found their way into a wide range of natural language processing tasks including those in the biomedical domain. While these vector representations successfully capture semantic and syntactic word relations, hidden patterns and trends in the data, they fail to offer interpretability. Interpretability is a key means to justification which is an integral part when it comes to biomedical applications. We present an inclusive study on interpretability of word embeddings in the medical domain, focusing on the role of sparse methods. Qualitative and quantitative measurements and metrics for interpretability of word vector representations are provided. For the quantitative evaluation, we introduce an extensive categorized dataset that can be used to quantify interpretability based on category theory. Intrinsic and extrinsic evaluation of the studied methods are also presented. As for the latter, we propose datasets which can be utilized for effective extrinsic evaluation of word vectors in the biomedical domain. Based on our experiments, it is seen that sparse word vectors show far more interpretability while preserving the performance of their original vectors in downstream tasks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2005.05114
- https://arxiv.org/pdf/2005.05114
- OA Status
- green
- References
- 18
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3021342956
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3021342956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2005.05114Digital Object Identifier
- Title
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Evaluating Sparse Interpretable Word Embeddings for Biomedical DomainWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2020Year of publication
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2020-05-11Full publication date if available
- Authors
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Mohammad Amin Samadi, Mohammad Sadegh Akhondzadeh, Sayed Jalal Zahabi, Mohammad Hossein Manshaei, Zeinab Maleki, Payman AdibiList of authors in order
- Landing page
-
https://arxiv.org/abs/2005.05114Publisher landing page
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https://arxiv.org/pdf/2005.05114Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2005.05114Direct OA link when available
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Interpretability, Word (group theory), Computer science, Artificial intelligence, Domain (mathematical analysis), Natural language processing, Range (aeronautics), Machine learning, Mathematics, Materials science, Geometry, Composite material, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.focusing | 76 |
| abstract_inverted_index.integral | 54 |
| abstract_inverted_index.language | 12 |
| abstract_inverted_index.methods. | 82 |
| abstract_inverted_index.original | 172 |
| abstract_inverted_index.patterns | 33 |
| abstract_inverted_index.quantify | 112 |
| abstract_inverted_index.semantic | 27 |
| abstract_inverted_index.utilized | 139 |
| abstract_inverted_index.Intrinsic | 118 |
| abstract_inverted_index.effective | 141 |
| abstract_inverted_index.extensive | 104 |
| abstract_inverted_index.extrinsic | 120, 142 |
| abstract_inverted_index.including | 15 |
| abstract_inverted_index.inclusive | 65 |
| abstract_inverted_index.introduce | 102 |
| abstract_inverted_index.provided. | 96 |
| abstract_inverted_index.syntactic | 29 |
| abstract_inverted_index.biomedical | 19, 60, 149 |
| abstract_inverted_index.downstream | 175 |
| abstract_inverted_index.embeddings | 1, 71 |
| abstract_inverted_index.evaluation | 121, 143 |
| abstract_inverted_index.presented. | 128 |
| abstract_inverted_index.preserving | 167 |
| abstract_inverted_index.processing | 13 |
| abstract_inverted_index.relations, | 31 |
| abstract_inverted_index.Qualitative | 83 |
| abstract_inverted_index.categorized | 105 |
| abstract_inverted_index.evaluation, | 100 |
| abstract_inverted_index.performance | 169 |
| abstract_inverted_index.experiments, | 154 |
| abstract_inverted_index.measurements | 86 |
| abstract_inverted_index.quantitative | 85, 99 |
| abstract_inverted_index.successfully | 25 |
| abstract_inverted_index.applications. | 61 |
| abstract_inverted_index.justification | 50 |
| abstract_inverted_index.representations | 24, 94 |
| abstract_inverted_index.Interpretability | 44 |
| abstract_inverted_index.interpretability | 68, 90, 113, 165 |
| abstract_inverted_index.interpretability. | 43 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |