Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach (2022.coling-1)
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| Challenge: | Experimental results show that a neural architecture that combines both modalities yields better results. |
| Approach: | They propose a neural architecture that combines both modalities to solve the problem of determining whether people are located in tweets. |
| Outcome: | The proposed model combines both modalities to produce better results . |
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| Challenge: | a number of studies have focused on detecting named entities in written language. |
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| Challenge: | Existing studies show that authors of tweets possess objects they tweet about. |
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Point-of-Interest Type Inference from Social Media Text (2020.aacl-main)
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| Challenge: | Social media posts often contain images to provide content, provide context, or express feelings. |
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| Challenge: | Recent studies have focused on identifying informative tweets by individuals affected by a crisis, without considering their specific types. |
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Visual Attention Model for Name Tagging in Multimodal Social Media (P18-1)
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| Challenge: | Name tagging is a key task for language understanding, but is often limited by the short textual components. |
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| Challenge: | Existing benchmarks show coarse granularity, linguistic bias, and a neglect of multimodal privacy risks. |
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| Challenge: | Existing studies on how images are structured with texts to form coherent meanings in human cognition have not addressed the problem. |
| Approach: | They propose a concept of cross-modality discourse which defines how human readers couple image and text understandings. |
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Building a Multimodal Entity Linking Dataset From Tweets (2020.lrec-1)
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| Challenge: | Entity linking is a task that aims at associating an entity mention with a unique entity in a knowledge base. |
| Approach: | They propose a method to quasi-automatically build annotated datasets to evaluate methods on the Entity Linking task. |
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