Enhancing metabolic event extraction performance with multitask learning concept

Kongburan, Wutthipong; Padungweang, Praisan; Krathu, Worarat; Chan, Jonathan H.

doi:10.1016/j.jbi.2019.103156

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…In the formal case, where n tasks or subsets of them have relationship to each other (conventional deep learning approaches seek to solve only one task with one particular model), but not exactly the same ones, MTL can enhance the learning of a specific model by using the information contained in all n tasks (Wu et al , 2020). This research inspired and laid the groundwork for much of MTL's work, by showing feasible and significant early results (Kongburan et al , 2019). Crichton et al (2017) had presented three models, which were multi-output multitask model, MTM and STM, for BioNER.…”

Section: Related Workmentioning

confidence: 97%

ABEE: automated bio entity extraction from biomedical text documents

Kumar

Sharaff

2023

DTA

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PurposeThe purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.Design/methodology/approachIn the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.FindingsThe proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.Research limitations/implicationsAs such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.Practical implicationsAs far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.Social implicationsDuring the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.Originality/valueIn this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

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Section: Related Workmentioning

confidence: 97%

ABEE: automated bio entity extraction from biomedical text documents

Kumar

Sharaff

2023

DTA

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“…The dataset n2c2/OHNLP Track on Clinical Semantic Textual Similarity (Clini-calSTS) 4 provides pairs of clinical text fragments, which are unrecognizable sentences extracted from clinical notes. The task is to assign a numerical score to each pair of sentences to express their semantic similarity.…”

Section: Datasetsmentioning

confidence: 99%

“…Embedding words in a continuous semantic space has an important impact on many NLP tasks [4][5][6]. Mikolov et al [7] used word co-occurrence to train word vectors iteratively and proposed the Word2Vec model.…”

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confidence: 99%

Refining electronic medical records representation in manifold subspace

et al. 2022

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Background Electronic medical records (EMR) contain detailed information about patient health. Developing an effective representation model is of great significance for the downstream applications of EMR. However, processing data directly is difficult because EMR data has such characteristics as incompleteness, unstructure and redundancy. Therefore, preprocess of the original data is the key step of EMR data mining. The classic distributed word representations ignore the geometric feature of the word vectors for the representation of EMR data, which often underestimate the similarities between similar words and overestimate the similarities between distant words. This results in word similarity obtained from embedding models being inconsistent with human judgment and much valuable medical information being lost. Results In this study, we propose a biomedical word embedding framework based on manifold subspace. Our proposed model first obtains the word vector representations of the EMR data, and then re-embeds the word vector in the manifold subspace. We develop an efficient optimization algorithm with neighborhood preserving embedding based on manifold optimization. To verify the algorithm presented in this study, we perform experiments on intrinsic evaluation and external classification tasks, and the experimental results demonstrate its advantages over other baseline methods. Conclusions Manifold learning subspace embedding can enhance the representation of distributed word representations in electronic medical record texts. Reduce the difficulty for researchers to process unstructured electronic medical record text data, which has certain biomedical research value.

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“…Besides event extraction, joint classification models can also be trained with other NLP tasks, like named entity recognition, event co-reference resolution, event relation extraction and etc. Some researchers have proposed to train a single model to jointly execute these tasks [128]- [132]. For example, Li et al [128] proposed a framework to jointly execute these tasks together with event extraction in a single model.…”

Section: Joint Classification Modelmentioning

confidence: 99%

A Survey of Event Extraction From Text

2019

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Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions. INDEX TERMS Event extraction, event extraction tasks, event corpus, natural language processing.

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Enhancing metabolic event extraction performance with multitask learning concept

Cited by 8 publications

References 20 publications

ABEE: automated bio entity extraction from biomedical text documents

ABEE: automated bio entity extraction from biomedical text documents

Refining electronic medical records representation in manifold subspace

A Survey of Event Extraction From Text

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