AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes Article Swipe
Barry Menglong Yao
,
Yu Chen
,
Qifan Wang
,
Sijia Wang
,
Min‐Qian Liu
,
Zhiyang Xu
,
Licheng Yu
,
Lifu Huang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.14725
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.14725
We propose attribute-aware multimodal entity linking, where the input consists of a mention described with a text paragraph and images, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also accompanied by a text description, visual images, and a collection of attributes that present the meta-information of the entity in a structured format. To facilitate this research endeavor, we construct AMELI, encompassing a new multimodal entity linking benchmark dataset that contains 16,735 mentions described in text and associated with 30,472 images, and a multimodal knowledge base that covers 34,690 entities along with 177,873 entity images and 798,216 attributes. To establish baseline performance on AMELI, we experiment with several state-of-the-art architectures for multimodal entity linking and further propose a new approach that incorporates attributes of entities into disambiguation. Experimental results and extensive qualitative analysis demonstrate that extracting and understanding the attributes of mentions from their text descriptions and visual images play a vital role in multimodal entity linking. To the best of our knowledge, we are the first to integrate attributes in the multimodal entity linking task. The programs, model checkpoints, and the dataset are publicly available at https://github.com/VT-NLP/Ameli.
Related Topics
Concepts
Computer science
Benchmark (surveying)
Entity linking
Construct (python library)
Baseline (sea)
Set (abstract data type)
Information retrieval
Task (project management)
Process (computing)
Knowledge base
Image (mathematics)
Artificial intelligence
Natural language processing
Data mining
Operating system
Geography
Oceanography
Geology
Economics
Geodesy
Management
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.14725
- https://arxiv.org/pdf/2305.14725
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378473861
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4378473861Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2305.14725Digital Object Identifier
- Title
-
AMELI: Enhancing Multimodal Entity Linking with Fine-Grained AttributesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-24Full publication date if available
- Authors
-
Barry Menglong Yao, Yu Chen, Qifan Wang, Sijia Wang, Min‐Qian Liu, Zhiyang Xu, Licheng Yu, Lifu HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.14725Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.14725Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2305.14725Direct OA link when available
- Concepts
-
Computer science, Benchmark (surveying), Entity linking, Construct (python library), Baseline (sea), Set (abstract data type), Information retrieval, Task (project management), Process (computing), Knowledge base, Image (mathematics), Artificial intelligence, Natural language processing, Data mining, Operating system, Geography, Oceanography, Geology, Economics, Geodesy, Management, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.new | 74, 129 |
| abstract_inverted_index.our | 172 |
| abstract_inverted_index.the | 7, 21, 26, 55, 58, 149, 169, 176, 182, 192 |
| abstract_inverted_index.(KB) | 35 |
| abstract_inverted_index.also | 40 |
| abstract_inverted_index.base | 34, 96 |
| abstract_inverted_index.best | 170 |
| abstract_inverted_index.each | 37 |
| abstract_inverted_index.from | 30, 153 |
| abstract_inverted_index.goal | 22 |
| abstract_inverted_index.into | 136 |
| abstract_inverted_index.play | 160 |
| abstract_inverted_index.role | 163 |
| abstract_inverted_index.text | 16, 44, 86, 155 |
| abstract_inverted_index.that | 53, 80, 97, 131, 145 |
| abstract_inverted_index.this | 66 |
| abstract_inverted_index.with | 14, 89, 102, 117 |
| abstract_inverted_index.along | 101 |
| abstract_inverted_index.first | 177 |
| abstract_inverted_index.input | 8 |
| abstract_inverted_index.model | 189 |
| abstract_inverted_index.task. | 186 |
| abstract_inverted_index.their | 154 |
| abstract_inverted_index.vital | 162 |
| abstract_inverted_index.where | 6, 36 |
| abstract_inverted_index.16,735 | 82 |
| abstract_inverted_index.30,472 | 90 |
| abstract_inverted_index.34,690 | 99 |
| abstract_inverted_index.AMELI, | 71, 114 |
| abstract_inverted_index.covers | 98 |
| abstract_inverted_index.entity | 4, 29, 38, 59, 76, 104, 123, 166, 184 |
| abstract_inverted_index.images | 105, 159 |
| abstract_inverted_index.target | 28 |
| abstract_inverted_index.visual | 46, 158 |
| abstract_inverted_index.177,873 | 103 |
| abstract_inverted_index.798,216 | 107 |
| abstract_inverted_index.dataset | 79, 193 |
| abstract_inverted_index.format. | 63 |
| abstract_inverted_index.further | 126 |
| abstract_inverted_index.images, | 19, 47, 91 |
| abstract_inverted_index.linking | 77, 124, 185 |
| abstract_inverted_index.mention | 12 |
| abstract_inverted_index.predict | 25 |
| abstract_inverted_index.present | 54 |
| abstract_inverted_index.propose | 1, 127 |
| abstract_inverted_index.results | 139 |
| abstract_inverted_index.several | 118 |
| abstract_inverted_index.analysis | 143 |
| abstract_inverted_index.approach | 130 |
| abstract_inverted_index.baseline | 111 |
| abstract_inverted_index.consists | 9 |
| abstract_inverted_index.contains | 81 |
| abstract_inverted_index.entities | 100, 135 |
| abstract_inverted_index.linking, | 5 |
| abstract_inverted_index.linking. | 167 |
| abstract_inverted_index.mentions | 83, 152 |
| abstract_inverted_index.publicly | 195 |
| abstract_inverted_index.research | 67 |
| abstract_inverted_index.available | 196 |
| abstract_inverted_index.benchmark | 78 |
| abstract_inverted_index.construct | 70 |
| abstract_inverted_index.described | 13, 84 |
| abstract_inverted_index.endeavor, | 68 |
| abstract_inverted_index.establish | 110 |
| abstract_inverted_index.extensive | 141 |
| abstract_inverted_index.integrate | 179 |
| abstract_inverted_index.knowledge | 33, 95 |
| abstract_inverted_index.paragraph | 17 |
| abstract_inverted_index.programs, | 188 |
| abstract_inverted_index.associated | 88 |
| abstract_inverted_index.attributes | 52, 133, 150, 180 |
| abstract_inverted_index.collection | 50 |
| abstract_inverted_index.experiment | 116 |
| abstract_inverted_index.extracting | 146 |
| abstract_inverted_index.facilitate | 65 |
| abstract_inverted_index.knowledge, | 173 |
| abstract_inverted_index.multimodal | 3, 32, 75, 94, 122, 165, 183 |
| abstract_inverted_index.structured | 62 |
| abstract_inverted_index.accompanied | 41 |
| abstract_inverted_index.attributes. | 108 |
| abstract_inverted_index.demonstrate | 144 |
| abstract_inverted_index.performance | 112 |
| abstract_inverted_index.qualitative | 142 |
| abstract_inverted_index.Experimental | 138 |
| abstract_inverted_index.checkpoints, | 190 |
| abstract_inverted_index.description, | 45 |
| abstract_inverted_index.descriptions | 156 |
| abstract_inverted_index.encompassing | 72 |
| abstract_inverted_index.incorporates | 132 |
| abstract_inverted_index.architectures | 120 |
| abstract_inverted_index.corresponding | 27 |
| abstract_inverted_index.understanding | 148 |
| abstract_inverted_index.attribute-aware | 2 |
| abstract_inverted_index.disambiguation. | 137 |
| abstract_inverted_index.meta-information | 56 |
| abstract_inverted_index.state-of-the-art | 119 |
| abstract_inverted_index.https://github.com/VT-NLP/Ameli. | 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |