Assessing citation integrity in biomedical publications: corpus annotation and NLP models.

MOTIVATION: Citations have a fundamental role in scholarly communication and assessment. Citation accuracy and transparency is crucial for the integrity of scientific evidence. In this work, we focus on quotation errors, errors in citation content that can distort the scientific evidence and that are hard to detect for humans. We construct a corpus and propose natural language processing (NLP) methods to identify such errors in biomedical publications. RESULTS: We manually annotated 100 highly-cited biomedical publications (reference articles) and citations to them. The annotation involved labeling citation context in the citing article, relevant evidence sentences in the reference article, and the accuracy of the citation. A total of 3063 citation instances were annotated (39.18% with accuracy errors). For NLP, we combined a sentence retriever with a fine-tuned claim verification model to label citations as ACCURATE, NOT_ACCURATE, or IRRELEVANT. We also explored few-shot in-context learning with generative large language models. The best performing model-which uses citation sentences as citation context, the BM25 model with MonoT5 reranker for retrieving top-20 sentences, and a fine-tuned MultiVerS model for accuracy label classification-yielded 0.59 micro-F1 and 0.52 macro-F1 score. GPT-4 in-context learning performed better in identifying accurate citations, but it lagged for erroneous citations (0.65 micro-F1, 0.45 macro-F1). Citation quotation errors are often subtle, and it is currently challenging for NLP models to identify erroneous citations. With further improvements, the models could serve to improve citation quality and accuracy. AVAILABILITY AND IMPLEMENTATION: We make the corpus and the best-performing NLP model publicly available at https://github.com/ScienceNLP-Lab/Citation-Integrity/.

Enhancing the coverage of SemRep using a relation classification approach.

OBJECTIVE: Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The broad-coverage natural language processing (NLP) tool SemRep has established a solid baseline for extracting subject-predicate-object triples from biomedical text and has served as the backbone of the Semantic MEDLINE Database (SemMedDB), a PubMed-scale repository of semantic triples. While SemRep achieves reasonable precision (0.69), its recall is relatively low (0.42). In this study, we aimed to enhance SemRep using a relation classification approach, in order to eventually increase the size and the utility of SemMedDB. METHODS: We combined and extended existing SemRep evaluation datasets to generate training data. We leveraged the pre-trained PubMedBERT model, enhancing it through additional contrastive pre-training and fine-tuning. We experimented with three entity representations: mentions, semantic types, and semantic groups. We evaluated the model performance on a portion of the SemRep Gold Standard dataset and compared it to SemRep performance. We also assessed the effect of the model on a larger set of 12K randomly selected PubMed abstracts. RESULTS: Our results show that the best model yields a precision of 0.62, recall of 0.81, and F1 score of 0.70. Assessment on 12K abstracts shows that the model could double the size of SemMedDB, when applied to entire PubMed. We also manually assessed the quality of 506 triples predicted by the model that SemRep had not previously identified, and found that 67% of these triples were correct. CONCLUSION: These findings underscore the promise of our model in achieving a more comprehensive coverage of relationships mentioned in biomedical literature, thereby showing its potential in enhancing various downstream applications of biomedical literature mining. Data and code related to this study are available at https://github.com/Michelle-Mings/SemRep_RelationClassification.