Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction

Le, Hoang-Quynh; Tran, Mai-Vu; Dang, Thanh Hai; Ha, Quang-Thuy; Collier, Nigel

doi:10.1093/database/baw102

Cited by 8 publications

(15 citation statements)

References 29 publications

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“…It was first proposed for coreference resolution [30] and then applied to other tasks such as temporal relations and quote attribution [31,32]. It has also been employed for various tasks in the biomedical domains, such as entity linking, coreference resolution, relation extraction, and concept normalization [33][34][35][36]. Our system can be seen as consisting of four sieves that capture different types of information: (1) context triggers, (2) short-range intra-sentence (i.e., intraclause) events, (3) long-range intra-sentence (i.e., crossclause) events, and (4) cross-sentence events.…”

Section: Multi-pass Sieve Architecturementioning

confidence: 99%

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Unsupervised inference of implicit biomedical events using context triggers

2020

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Background: Event extraction from the biomedical literature is one of the most actively researched areas in biomedical text mining and natural language processing. However, most approaches have focused on events within single sentence boundaries, and have thus paid much less attention to events spanning multiple sentences. The Bacteria-Biotope event (BB-event) subtask presented in BioNLP Shared Task 2016 is one such example; a significant amount of relations between bacteria and biotope span more than one sentence, but existing systems have treated them as false negatives because labeled data is not sufficiently large enough to model a complex reasoning process using supervised learning frameworks. Results: We present an unsupervised method for inferring cross-sentence events by propagating intra-sentence information to adjacent sentences using context trigger expressions that strongly signal the implicit presence of entities of interest. Such expressions can be collected from a large amount of unlabeled plain text based on simple syntactic constraints, helping to overcome the limitation of relying only on a small number of training examples available. The experimental results demonstrate that our unsupervised system extracts cross-sentence events quite well and outperforms all the state-of-the-art supervised systems when combined with existing methods for intra-sentence event extraction. Moreover, our system is also found effective at detecting long-distance intra-sentence events, compared favorably with existing high-dimensional models such as deep neural networks, without any supervised learning techniques. Conclusions: Our linguistically motivated inference model is shown to be effective at detecting implicit events that have not been covered by previous work, without relying on training data or curated knowledge bases. Moreover, it also helps to boost the performance of existing systems by allowing them to detect additional cross-sentence events. We believe that the proposed model offers an effective way to infer implicit information beyond sentence boundaries, especially when human-annotated data is not sufficient enough to train a robust supervised system.

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Section: Multi-pass Sieve Architecturementioning

confidence: 99%

“…This means that it is also easy to assess the contribution of each sieve to the overall performance. This is particularly useful when supervised learning does not work well, as discussed in the section on related work [30][31][32][33][34][35][36].…”

Section: Multi-pass Sieve Architecturementioning

confidence: 99%

Unsupervised inference of implicit biomedical events using context triggers

2020

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“…Despite some initial results, there are still limitations of recent approaches for inter sentence RE. The end-to-end model proposed in [ 23 ] resolved intra sentence relation classification partly by using a multi-pass sieve coreference resolution module. It has the drawback of strongly depending on the appearances of antecedent and anaphor representations of entities in the text since there are many inter sentence relations not expressed through anaphor.…”

Section: Introductionmentioning

confidence: 99%

Exploiting document graphs for inter sentence relation extraction

Can

Collier

2022

J Biomed Semant

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Background Most previous relation extraction (RE) studies have focused on intra sentence relations and have ignored relations that span sentences, i.e. inter sentence relations. Such relations connect entities at the document level rather than as relational facts in a single sentence. Extracting facts that are expressed across sentences leads to some challenges and requires different approaches than those usually applied in recent intra sentence relation extraction. Despite recent results, there are still limitations to be overcome. Results We present a novel representation for a sequence of consecutive sentences, namely document subgraph, to extract inter sentence relations. Experiments on the BioCreative V Chemical-Disease Relation corpus demonstrate the advantages and robustness of our novel system to extract both intra- and inter sentence relations in biomedical literature abstracts. The experimental results are comparable to state-of-the-art approaches and show the potential by demonstrating the effectiveness of graphs, deep learning-based model, and other processing techniques. Experiments were also carried out to verify the rationality and impact of various additional information and model components. Conclusions Our proposed graph-based representation helps to extract ∼50% of inter sentence relations and boosts the model performance on both precision and recall compared to the baseline model.

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“…Relation extraction is usually considered as a classification problem. Three kinds of approaches have been applied to extract medical relations: rule-based approaches (2, 3), shallow machine learning approaches (4, 5) and deep learning approaches (1, 6).…”

Section: Introductionmentioning

confidence: 99%

A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning

Chen

2019

Database

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The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.

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Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction

Cited by 8 publications

References 29 publications

Unsupervised inference of implicit biomedical events using context triggers

Unsupervised inference of implicit biomedical events using context triggers

Exploiting document graphs for inter sentence relation extraction

A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning

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