40 research outputs found
Results of the seventh edition of the BioASQ Challenge
The results of the seventh edition of the BioASQ challenge are presented in
this paper. The aim of the BioASQ challenge is the promotion of systems and
methodologies through the organization of a challenge on the tasks of
large-scale biomedical semantic indexing and question answering. In total, 30
teams with more than 100 systems participated in the challenge this year. As in
previous years, the best systems were able to outperform the strong baselines.
This suggests that state-of-the-art systems are continuously improving, pushing
the frontier of research.Comment: 17 pages, 2 figure
Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials
CLEF 2021 – Conference and Labs of the Evaluation Forum, September 21–24, 2021, Bucharest, Romania,There is a pressing need to exploit recent advances in natural language processing technologies, in
particular language models and deep learning approaches, to enable improved retrieval, classification
and ultimately access to information contained in multiple, heterogeneous types of documents. This is
particularly true for the field of biomedicine and clinical research, where medical experts and scientists
need to carry out complex search queries against a variety of document collections, including literature,
patents, clinical trials or other kind of content like EHRs. Indexing documents with structured controlled
vocabularies used for semantic search engines and query expansion purposes is a critical task for enabling
sophisticated user queries and even cross-language retrieval. Due to the complexity of the medical domain
and the use of very large hierarchical indexing terminologies, implementing efficient automatic systems
to aid manual indexing is extremely difficult. This paper provides a summary of the MESINESP task
results on medical semantic indexing in Spanish (BioASQ/ CLEF 2021 Challenge). MESINESP was carried
out in direct collaboration with literature content databases and medical indexing experts using the DeCS
vocabulary, a similar resource as MeSH terms. Seven participating teams used advanced technologies
including extreme multilabel classification and deep language models to solve this challenge which can
be viewed as a multi-label classification problem. MESINESP resources, we have released a Gold Standard
collection of 243,000 documents with a total of 2179 manual annotations divided in train, development
and test subsets covering literature, patents as well as clinical trial summaries, under a cross-genre
training and data labeling scenario. Manual indexing of the evaluation subsets was carried out by three
independent experts using a specially developed indexing interface called ASIT. Additionally, we have
published a collection of large-scale automatic semantic annotations based on NER systems of these
documents with mentions of drugs/medications (170,000), symptoms (137,000), diseases (840,000) and
clinical procedures (415,000). In addition to a summary of the used technologies by the teams, this paperS
Large scale biomedical texts classification: a kNN and an ESA-based approaches
With the large and increasing volume of textual data, automated methods for
identifying significant topics to classify textual documents have received a
growing interest. While many efforts have been made in this direction, it still
remains a real challenge. Moreover, the issue is even more complex as full
texts are not always freely available. Then, using only partial information to
annotate these documents is promising but remains a very ambitious issue.
MethodsWe propose two classification methods: a k-nearest neighbours
(kNN)-based approach and an explicit semantic analysis (ESA)-based approach.
Although the kNN-based approach is widely used in text classification, it needs
to be improved to perform well in this specific classification problem which
deals with partial information. Compared to existing kNN-based methods, our
method uses classical Machine Learning (ML) algorithms for ranking the labels.
Additional features are also investigated in order to improve the classifiers'
performance. In addition, the combination of several learning algorithms with
various techniques for fixing the number of relevant topics is performed. On
the other hand, ESA seems promising for this classification task as it yielded
interesting results in related issues, such as semantic relatedness computation
between texts and text classification. Unlike existing works, which use ESA for
enriching the bag-of-words approach with additional knowledge-based features,
our ESA-based method builds a standalone classifier. Furthermore, we
investigate if the results of this method could be useful as a complementary
feature of our kNN-based approach.ResultsExperimental evaluations performed on
large standard annotated datasets, provided by the BioASQ organizers, show that
the kNN-based method with the Random Forest learning algorithm achieves good
performances compared with the current state-of-the-art methods, reaching a
competitive f-measure of 0.55% while the ESA-based approach surprisingly
yielded reserved results.ConclusionsWe have proposed simple classification
methods suitable to annotate textual documents using only partial information.
They are therefore adequate for large multi-label classification and
particularly in the biomedical domain. Thus, our work contributes to the
extraction of relevant information from unstructured documents in order to
facilitate their automated processing. Consequently, it could be used for
various purposes, including document indexing, information retrieval, etc.Comment: Journal of Biomedical Semantics, BioMed Central, 201