Automatic semantic classification of scientific literature according to the hallmarks of cancer

Baker, Simon; Silins, Ilona; Guo, Yufan; Ali, Imran; Högberg, Johan; Stenius, Ulla; Korhonen, Anna

doi:10.1093/bioinformatics/btv585

Cited by 69 publications

(64 citation statements)

References 27 publications

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“…We evaluate our word representations using two established biomedical datasets for text classification: the Hallmarks of Cancer (HOC) (Baker et al, 2015 and the Exposure taxonomy (EXP) (Larsson et al, 2017). We evaluate each based on their document-level and sentence-level classifications.…”

Section: Task 1: Text Classificationmentioning

confidence: 99%

“…Introduced by Weinberg and Hanahan (2000), it has been widely used in biomedical NLP, including as part of the BioNLP Shared Task 2013, "Cancer Genetics task" (Pyysalo et al, 2013b). Baker et al (2015 have released an expert-annotated dataset of cancer hallmark classifications for both sentences and documents in PubMed. The data consists of multi-labelled documents and sentences using a taxonomy of 37 classes.…”

Section: Task 1: Text Classificationmentioning

confidence: 99%

“…In the past, lexical verb classes have been successfully shown to improve the performance classifiers in a variety of tasks and down stream applications in the biomedical domain; such as relation extraction (Sharma et al, 2010), biomedical fact extraction (Rupp et al, 2010), text classification for cancer (Baker et al, 2015), biomedical discourse analysis (Cox et al, 2017), and biomedical information retrieval (Mahalakshmi, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Enhancing biomedical word embeddings by retrofitting to verb clusters

Chiu

Baker

Palmer

et al. 2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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Verbs play a fundamental role in many biomedical tasks and applications such as relation and event extraction. We hypothesize that performance on many downstream tasks can be improved by aligning the input pretrained embeddings according to semantic verb classes. In this work, we show that by using semantic clusters for verbs, a large lexicon of verb classes derived from biomedical literature, we are able to improve the performance of common pretrained embeddings in downstream tasks by retrofitting them to verb classes. We present a simple and computationally efficient approach using a widely-available "off-theshelf" retrofitting algorithm to align pretrained embeddings according to semantic verb clusters. We achieve state-of-the-art results on text classification and relation extraction tasks.

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Section: Task 1: Text Classificationmentioning

confidence: 99%

Section: Task 1: Text Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing biomedical word embeddings by retrofitting to verb clusters

Chiu

Baker

Palmer

et al. 2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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“…biological processes) from cancer-domain texts. Baker et al (2016) have released an expert-annotated dataset for cancer hallmark classification for both sentences and documents from PubMed. The data consists of multilabelled documents and sentences using a taxonomy of 37 classes.…”

Section: Datamentioning

confidence: 99%

Initializing neural networks for hierarchical multi-label text classification

Baker¹,

Korhonen²

2017

BioNLP 2017

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Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary classifier is trained for each label in the taxonomy or ontology where all instances not belonging to the class are considered negative examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the additional computational cost of training parallel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such as hypernymy. This approach elegantly lends itself to hierarchical classification. We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentence-and document-level classification. Our evaluation shows promising results for this approach.

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“…Over recent years, TM techniques have enabled large-scale information extraction and knowledge discovery [5], and have been successfully applied in real life tasks. For example, TM techniques have been applied in cancer research [6, 7] and cancer chemical risk assessment [8, 9], toxicogenomics [10], and drug effects/safety [11, 12]. …”

Section: Introductionmentioning

confidence: 99%

Text mining for improved exposure assessment

Larsson

Baker²,

Silins

et al. 2017

PLoS ONE

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Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure information available from web-based databases, such as PubMed, is undoubtedly a great asset to the scientific community. However, manual retrieval of relevant published information is an extremely time consuming task and overviewing the data is nearly impossible. Here, we present the development of an automatic classifier for chemical exposure information. First, nearly 3700 abstracts were manually annotated by an expert in exposure sciences according to a taxonomy exclusively created for exposure information. Natural Language Processing (NLP) techniques were used to extract semantic and syntactic features relevant to chemical exposure text. Using these features, we trained a supervised machine learning algorithm to automatically classify PubMed abstracts according to the exposure taxonomy. The resulting classifier demonstrates good performance in the intrinsic evaluation. We also show that the classifier improves information retrieval of chemical exposure data compared to keyword-based PubMed searches. Case studies demonstrate that the classifier can be used to assist researchers by facilitating information retrieval and classification, enabling data gap recognition and overviewing available scientific literature using chemical-specific publication profiles. Finally, we identify challenges to be addressed in future development of the system.

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Automatic semantic classification of scientific literature according to the hallmarks of cancer

Abstract: simon.baker@cl.cam.ac.uk.

Cited by 69 publications

References 27 publications

Enhancing biomedical word embeddings by retrofitting to verb clusters

Enhancing biomedical word embeddings by retrofitting to verb clusters

Initializing neural networks for hierarchical multi-label text classification

Text mining for improved exposure assessment

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