A set of domain rules and a deep network for protein coreference resolution

Li, Chen; Rao, Zhiqiang; Zhang, Xiangrong

doi:10.1093/database/bay065

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…Typical works of Bio-entity coreference resolution use rule-based [4,5,6] and hybrid methods [7,8], which rely on syntactic features and are limited to a specific corpus. Recently, neural network-based methods for automatically identifying coreferences have received widespread attention.…”

Section: Context and Motivationmentioning

confidence: 99%

“…Some neural networkbased systems have been developed that integrate domain-specific information through pre-trained embeddings and static bio-related features. [6,9,10].…”

Section: Context and Motivationmentioning

confidence: 99%

“…The work can be categorized into three classes. 1) rule and feature-based models [4,5,6], which heavily rely on syntactic parsers to extract manually crafted features and rules. 2) hybrid models [7,8], which combine rule-based and machine learning-based methods for biomedical coreference resolution 3) neural networkbased models [6,9,10], which use deep learning or neural networks to solve the problem automatically with domain-specific information integration, typically by 1 pre-trained embeddings and some biomedical features.…”

Section: Related Workmentioning

confidence: 99%

“…First, compared with [6], BioNeu performs much better in the reduction of spurious links, which indicates that the domain-specific information pre-trained on large-scale language models helps the model to predict more precisely. Second, compared with those neural network-based baselines [6]-neural and BioNeu, the proposed SFA model illustrates that the introduction of the Feature Attention mechanism further improves the model, greatly increasing the number of correct predictions and reducing the two errors. This shows that the distinction of the identical or similar mentions based on context can help the system learn more precisely and make more accurate predictions.…”

Section: Error Analysismentioning

confidence: 99%

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Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Zhou

et al. 2021

Preprint

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Background: Bio-entity Coreference resolution is an important task to gain a complete understanding of biomedical texts automatically. Previous neural network-based studies on this topic are domain system based methods which rely on some domain-specific information integration. However, for the identical mentions, this may lead to misleading information, as the model tends to get similar or even identical representations, which further leads to wrongful predictions. Results: we propose a new context-aware Feature Attention model to distinguish identical mentions effectively to better resolve coreference. The new model can represent identical mentions based on different contexts by adaptively exploiting features effectively. The proposed model substantially outperforms the state-of-the-art baselines on the BioNLP dataset with a 64.0% F1 score and further demonstrates superior performance on the differential representation and coreferential link of identical mentions. Conclusion: The context-aware Feature Attention model adaptively exploit features and represent identical mentions according to different contexts, which significantly makes the system obtain semantic information effectively and make more accurate predictions. Considering that this approach is still limited when context information is insufficient, we expect to utilize such information more fine-grained with the help of the external knowledge base in coreference resolution.

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Section: Context and Motivationmentioning

confidence: 99%

“…Some neural networkbased systems have been developed that integrate domain-specific information through pre-trained embeddings and static bio-related features. [6,9,10].…”

Section: Context and Motivationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Error Analysismentioning

confidence: 99%

See 2 more Smart Citations

Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Zhou

et al. 2021

Preprint

Self Cite

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“…In general, two types of hybrid systems are developed to combine rule-based algorithms with machine learning techniques. One approach utilizes rules to verify the machine learning output [38], [39], while a more prevailing approach leverages on rule-based algorithms to precisely identify the desired features to feed machine learning models. In the medical domain, such methods have been explored to solve NLP tasks such as clinical text classification [40], [41], entity extraction [42], [43], and relation detection [44]- [47].…”

Section: Related Workmentioning

confidence: 99%

Regular Expression Based Medical Text Classification Using Constructive Heuristic Approach

Cui

Bai

et al. 2019

IEEE Access

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Medical text classification assigns medical related text into different categories such as topics or disease types. Machine learning based techniques have been widely used to perform such tasks despite the obvious drawback in such ''black box'' approach, leaving no easy way to fine-tune the resultant model for better performance. We propose a novel constructive heuristic approach to generate a set of regular expressions that can be used as effective text classifiers. The main innovation of our approach is that we develop a novel regular expression based text classifier with both satisfactory classification performance and excellent interpretability. We evaluate our framework on real-world medical data provided by our collaborator, one of the largest online healthcare providers in the market, and observe the high performance and consistency of this approach. Experimental results show that the machine-generated regular expressions can be effectively used in conjunction with machine learning techniques to perform medical text classification tasks. The proposed methodology improves the performance of baseline methods (Naive Bayes and Support Vector Machines) by 9% in precision and 4.5% in recall. We also evaluate the performance of modified regular expressions by human experts and demonstrate the potential of practical applications using the proposed method. INDEX TERMS Regular expressions, text classification, constructive heuristic method.

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A hybrid medical text classification framework: Integrating attentive rule construction and neural network

Cui

et al. 2021

Neurocomputing

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The main objective of this work is to improve the quality and transparency of the medical text classification solutions. Conventional text classification methods provide users with only a restricted mechanism (based on frequency) for selecting features. In this paper, a three-stage hybrid method combining the threshold-gated attentive bi-directional Long Short-Term Memory (ABLSTM) and the regular expression based classifier is proposed for medical text classification tasks. The bi-directional Long Short-Term Memory (LSTM) architecture with an attention layer allows the network to weigh words according to their perceived importance and focus on crucial parts of a sentence. Feature words (or keywords) extracted by ABLSTM model are utilized to guide the regular expression rule construction. Our proposed approach leverages the advantages of both the interpretability of rule-based algorithms and the computational power of deep learning approaches for a production-ready scenario. Experimental results on real-world medical online query data clearly validate the superiority of our system in selecting domain-specific and topic-related features. Results show that the proposed approach achieves an accuracy of 0.89 and an F 1 -score of 0.92 respectively. Furthermore, our experimentation also illustrates the versatility of regular expressions as a user-level tool for focusing on desired patterns and providing interpretable solutions for human modification.

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A set of domain rules and a deep network for protein coreference resolution

Cited by 10 publications

References 19 publications

Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Regular Expression Based Medical Text Classification Using Constructive Heuristic Approach

A hybrid medical text classification framework: Integrating attentive rule construction and neural network

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