Papers with text corpus
Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance. |
| Approach: | They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning. |
| Outcome: | The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods. |
DRAGOn: Designing RAG On Periodically Updated Corpus (2026.eacl-srw)
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Fedor Chernogorskii, Sergei Averkiev, Liliya Kudraleeva, Zaven Martirosian, Maria Tikhonova, Valentin Malykh, Alena Fenogenova
| Challenge: | Existing methods for evaluating RAG systems are labor-intensive and difficult to maintain. |
| Approach: | They propose a method to design a RAG benchmark on a regularly updated corpus. |
| Outcome: | The proposed method uses a regularly updated corpus to evaluate RAG models. |
Fine-Grained Evaluation for Entity Linking (D19-1)
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| Challenge: | Entity Linking (EL) is an Information Extraction task that identifies entity mentions in a text corpus and associates them with an unambiguous identifier in KBs such as Wikipedia, BabelNet, DBpedia, Wikidata and YAGO. |
| Approach: | They propose a fine-grained categorization of different types of entity mentions and links and propose 'fuzzy recall' metric to address the lack of consensus and compare a selection of online EL systems. |
| Outcome: | The proposed task offers a bridge between unstructured text and structured KBs, where EL has applications for semantic search, document classification, relation extraction, and more. |
myMediCon: End-to-End Burmese Automatic Speech Recognition for Medical Conversations (2024.lrec-main)
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| Challenge: | Existing medical conversation speech corpora for Burmese are limited, despite advances in ASR. |
| Approach: | They propose to use a manually curated medical conversation speech corpus for Burmese to examine the performance of ASR models. |
| Outcome: | The proposed model outperforms the Transformer model and the Recurrent Neural Network (RNN) models. |