Papers with CDR
MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition (2025.acl-srw)
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Shanshan Liu, Noriki Nishida, Rumana Ferdous Munne, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Existing concepts recognition methods that rely on explicit mention identification fail to capture complex concepts not explicitly stated in the text. |
| Approach: | They propose a framework that reformulates concept recognition as an indexing-recognition task. |
| Outcome: | The proposed framework reduces computational requirements and improves recognition efficiency in low-resource settings. |
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction (2021.findings-acl)
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| Challenge: | Existing studies on document-level relation extraction focus on sentencelevel RE, but recent studies reveal that a large number of relations can actually be expressed through multiple sentences, which necessitates document- level RE. |
| Approach: | They propose a document-level relation extraction model that captures local and global contextual information as well as close and distant mention interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on three widely used datasets, namely DocRED, CDR, and GDA. |
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
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| Challenge: | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a document. |
| Approach: | They propose a document-level relation extraction framework that captures and exploits instructive information by adding extra syntactic information into text representations. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |
Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation (2025.coling-main)
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| Challenge: | Existing approaches to cross-domain recommendation (CDR) draw on historical purchase records or reviews to generate user representations. |
| Approach: | They propose a model that integrates preferences from coarse to fine levels to improve recommendations for cold-start users. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on three CDR tasks. |
Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval (2025.emnlp-industry)
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| Challenge: | Our work identifies unique challenges in conversational data retrieval . large language model-based systems operate through open-ended interactions without predefined specifications. |
| Approach: | They propose a benchmark to evaluate systems that retrieve conversation data for product insights. |
| Outcome: | The benchmark provides a reliable standard for measuring conversational data retrieval performance. |
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction (2022.naacl-main)
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| Challenge: | Existing methods for relation extraction only implicitly learn to model relevant contexts and entity types while being trained for RE. |
| Approach: | They propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE. |
| Outcome: | The proposed method outperforms the runner-up method on three benchmarks by 5.04% . textual contexts and entity types are the major information sources that lead to the success of previous approaches. |
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning (2022.coling-1)
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| Challenge: | Existing document-level relation extraction methods are sparse in relational entity pairs and the representation of entity pairs is insufficient. |
| Approach: | They propose a Pair-Aware and Entity-Enhanced(PAEE) model to solve two challenges . they propose predicting potential relational entity pairs and assembling directional entity pairs . |
| Outcome: | The proposed model can obtain state-of-the-art performance on four benchmark datasets . it can predict potential relational entity pairs and assemble directional entity pairs . |
CDRNN: Discovering Complex Dynamics in Human Language Processing (2021.acl-long)
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| Challenge: | Behavioral and fMRI experiments reveal detailed and plausible estimates of human language processing dynamics . central questions in psycholinguistics concern the mental processes involved in incremental human language understanding . |
| Approach: | They propose a continuous-time deconvolutional regressive neural network that captures time-varying, non-linear, and delayed influences of predictors on the response. |
| Outcome: | The proposed neural network captures time-varying, non-linear, and delayed influences on the response . Behavioral and fMRI experiments show it generalizes better than baselines . |
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (D18-1)
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| Challenge: | Existing large labeled text datasets contain labels for multiple subsets of biomedical entity types, but it is rare to find large labeling datasets containing all desired entity types together. |
| Approach: | They propose a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. |
| Outcome: | The proposed method improves NER F1 over training in isolation on biocreative V CDR, biocreativ VI ChemProt and MedMentions datasets. |