Mining document, concept, and term associations for effective biomedical retrieval: introducing MeSH-enhanced retrieval models

Mao, Jin; Lü, Kun; Mu, Xiangming; Li, Gang

doi:10.1007/s10791-015-9264-0

Cited by 9 publications

(4 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main underlying explanation is related to the limited expressiveness of concepts and/or the inaccuracy of the concept extraction method. More recently, an emerging line of work consists in enhancing the text-to-text matching model using evidence from external resources [16,11,26]. All of these contributions share the same goal: making inferences about the associations between raw data and the concept layer in the resource by building a relevance model.…”

Section: On the Semantic Gap Problem In Medical Searchmentioning

confidence: 99%

Learning Concept-Driven Document Embeddings for Medical Information Search

Nguyen

Tamine

Soulier

et al. 2017

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Many medical tasks such as self-diagnosis, health-care assessment, and clinical trial patient recruitment involve the usage of information access tools. A key underlying step to achieve such tasks is the document-to-document matching which mostly fails to bridge the gap identified between raw level representations of information in documents and high-level human interpretation. In this paper, we study how to optimize the document representation by leveraging neural-based approaches to capture latent representations built upon both validated medical concepts specified in an external resource as well as the used words. We experimentally show the effectiveness of our proposed model used as a support of two different medical search tasks, namely health search and clinical search for cohorts.

show abstract

Section: On the Semantic Gap Problem In Medical Searchmentioning

confidence: 99%

Learning Concept-Driven Document Embeddings for Medical Information Search

Nguyen

Tamine

Soulier

et al. 2017

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

show abstract

“…In the case where the quality of indexing outweighs the cost, one can design very specific concepts and index the information resource exhaustively. Alternatively, one can use the concepts as a middle layer between documents and free‐text terms in a generative model to enhance the bag‐of‐words representation, which has been shown to be effective in information retrieval (Mao, Lu, Mu, & Li, ), although this requires higher computation.…”

Section: Discussionmentioning

confidence: 99%

Toward effective automated weighted subject indexing: A comparison of different approaches in different environments

Lü

Mao

2017

Asso for Info Science & Tech

Self Cite

View full text Add to dashboard Cite

Subject indexing plays an important role in supporting subject access to information resources. Current subject indexing systems do not make adequate distinctions on the importance of assigned subject descriptors. Assigning numeric weights to subject descriptors to distinguish their importance to the documents can strengthen the role of subject metadata. Automated methods are more cost-effective. This study compares different automated weighting methods in different environments. Two evaluation methods were used to assess the performance. Experiments on three datasets in the biomedical domain suggest the performance of different weighting methods depends on whether it is an abstract or full text environment. Mutual information with bag-of-words representation shows the best average performance in the full text environment, while cosine with bag-of-words representation is the best in an abstract environment. The cosine measure has relatively consistent and robust performance. A direct weighting method, IDF (Inverse Document Frequency), can produce quick and reasonable estimates of the weights. Bag-of-words representation generally outperforms the concept-based representation. Further improvement in performance can be obtained by using the learning-to-rank method to integrate different weighting methods. This study follows up Lu and Mao

show abstract

“…For example, Oh et al [23] incorporated the structure of external collections to optimize pseudo relevance feedback. Mao et al [24] integrated a MeSH-enhanced concept layer into a language modeling framework to capture concept associations. Jalali et al [25] matched concept pairs between queries and documents using a semantic query expansion method.…”

Section: Related Workmentioning

confidence: 99%

A supervised term ranking model for diversity enhanced biomedical information retrieval

Lin

Yang

et al. 2019

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundThe number of biomedical research articles have increased exponentially with the advancement of biomedicine in recent years. These articles have thus brought a great difficulty in obtaining the needed information of researchers. Information retrieval technologies seek to tackle the problem. However, information needs cannot be completely satisfied by directly introducing the existing information retrieval techniques. Therefore, biomedical information retrieval not only focuses on the relevance of search results, but also aims to promote the completeness of the results, which is referred as the diversity-oriented retrieval.ResultsWe address the diversity-oriented biomedical retrieval task using a supervised term ranking model. The model is learned through a supervised query expansion process for term refinement. Based on the model, the most relevant and diversified terms are selected to enrich the original query. The expanded query is then fed into a second retrieval to improve the relevance and diversity of search results. To this end, we propose three diversity-oriented optimization strategies in our model, including the diversified term labeling strategy, the biomedical resource-based term features and a diversity-oriented group sampling learning method. Experimental results on TREC Genomics collections demonstrate the effectiveness of the proposed model in improving the relevance and the diversity of search results.ConclusionsThe proposed three strategies jointly contribute to the improvement of biomedical retrieval performance. Our model yields more relevant and diversified results than the state-of-the-art baseline models. Moreover, our method provides a general framework for improving biomedical retrieval performance, and can be used as the basis for future work.

show abstract

Mining document, concept, and term associations for effective biomedical retrieval: introducing MeSH-enhanced retrieval models

Cited by 9 publications

References 56 publications

Learning Concept-Driven Document Embeddings for Medical Information Search

Learning Concept-Driven Document Embeddings for Medical Information Search

Toward effective automated weighted subject indexing: A comparison of different approaches in different environments

A supervised term ranking model for diversity enhanced biomedical information retrieval

Contact Info

Product

Resources

About