Papers by Sören Auer
Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text (N19-1)
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Ahmad Sakor, Isaiah Onando Mulang’, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, Sören Auer
| Challenge: | Short texts challenge NLP tasks because they lack context or are partially malformed. |
| Approach: | They propose a method which maps entities and relations within a short text to Wikipedia mentions. |
| Outcome: | The proposed approach outperforms state-of-the-art methods for short text query inventories. |
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources (2020.lrec-1)
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| Challenge: | a dataset for scientific entity extraction, classification, and resolution has been developed . a generic conceptual formalism for scientific entities is feasible, the authors say . |
| Approach: | They propose a STEM-ECR dataset that provides a domain-independent benchmark for scientific entity extraction, classification, and resolution tasks. |
| Outcome: | The proposed dataset provides a benchmark for evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. |
Fine-tuning BERT with Focus Words for Explanation Regeneration (2020.starsem-1)
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| Challenge: | Existing approaches to explain the correct answer in multiple-choice QA are low in F-scores and lack of performance. |
| Approach: | They introduce a lightweight focus feature in a transformer-based NLP model and examine performance improvements. |
| Outcome: | The proposed model achieves the highest scores, second only to a computationally intensive system. |
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions (D19-53)
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| Challenge: | TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration tackles explanation generation for elementary science questions. |
| Approach: | They propose a hybrid pipelined machine learning model and rule-based system to address MIER-19 . they use a featurerich learning-to-rank machine learning and a rule-driven system to rerank the LTR model predictions. |
| Outcome: | The proposed model was ranked fourth in the evaluation, close to the second and third ranked teams, achieving 39.4% MAP. |
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology (2024.findings-eacl)
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| Challenge: | Scholarly communication in the digital age is facing significant challenges due to the overwhelming volume of publications. |
| Approach: | They propose to use Wikipedia infoboxes and structured Amazon product descriptions to create structured scholarly contribution summaries using text generation capabilities of LLMs. |
| Outcome: | The proposed model can be applied to complex IE tasks within terse domains like Science with 1000x fewer parameters than the state-of-the-art GPT-davinci. |