A hybrid deep-learning approach for complex biochemical named entity recognition

Liu, Jian; Gao, Lei; Guo, Sujie; Ding, Rui; Huang, Xin; Ye, Long; Meng, Qinghua; Nazari, Asef; Thiruvady, Dhananjay

doi:10.1016/j.knosys.2021.106958

Cited by 39 publications

(16 citation statements)

References 35 publications

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“…Deep learning has produced the most advanced performance on many natural language processing tasks, including NER. Liu et al [6] proposed a hybrid deep learning method in the medical field to improve the recognition accuracy of NER. Specifically, a two-way encoder representation model is used to extract the basic features of the text.…”

Section: Literature Reviewmentioning

confidence: 99%

Legal Text Recognition Using LSTM-CRF Deep Learning Model

2022

Computational Intelligence and Neuroscience

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In legal texts, named entity recognition (NER) is researched using deep learning models. First, the bidirectional (Bi)-long short-term memory (LSTM)-conditional random field (CRF) model for studying NER in legal texts is established. Second, different annotation methods are used to compare and analyze the entity recognition effect of the Bi-LSTM-CRF model. Finally, other objective loss functions are set to compare and analyze the entity recognition effect of the Bi-LSTM-CRF model. The research results show that the F1 value of the model trained on the word sequence labeling corpus on the named entity is 88.13%, higher than that of the word sequence labeling corpus. For the two types of entities, place names and organization names, the F1 values obtained by the Bi-LSTM-CRF model using word segmentation are 67.60% and 89.45%, respectively, higher than the F1 values obtained by the model using character segmentation. Therefore, the Bi-LSTM-CRF model using word segmentation is more suitable for recognizing extended entities. The parameter learning result using log-likelihood is better than that using the maximum interval criterion, and it is ideal for the Bi-LSTM-CRF model. This method provides ideas for the research of legal text recognition and has a particular value.

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Section: Literature Reviewmentioning

confidence: 99%

Legal Text Recognition Using LSTM-CRF Deep Learning Model

2022

Computational Intelligence and Neuroscience

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“…The model significantly outperformed the other two baseline models in terms of precision, recall, and F1 measures for the closed-domain IE tasks. In another study, Liu et al [18] presented a DL-based model called BERT-BiLSTM-MHATT-CRF (BBMC) [18] to identify and extract complex biochemical entities from scientific documents. The developed BBMC model comprises four different algorithms: (i) Bidirectional Encoder Representations from Transformers (BERT) model to extract features in the text, (ii) bidirectional long short-term memory (BiLSTM) to learn the context represented in the text, (iii) multi-head attention (MHATT) to extract chapter-level features, and (iv) conditional random field (CRF) to label the sequence tag.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results of this DL-based model showed significant improvement in identifying and extracting complex biochemical names from the datasets used for testing. According to [18], the DL-based method employing the BBMC model has significantly improved the extraction accuracy compared to the conventional BiLSTM-CRF algorithm. The study also concluded that DL-based methods can effectively identify and extract complex information from textual data.…”

Section: Introductionmentioning

confidence: 99%

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

et al. 2023

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Information extraction (IE) is a challenging task, particularly when dealing with highly heterogeneous data. State-of-the-art data mining technologies struggle to process information from textual data. Therefore, various IE techniques have been developed to enable the use of IE for textual data. However, each technique differs from one another because it is designed for different data types and has different target information to be extracted. This study investigated and described the most contemporary methods for extracting information from textual data, emphasizing their benefits and shortcomings. To provide a holistic view of the domain, this comprehensive systematic literature review employed a systematic mapping process to summarize studies published in the last six years (from 2017 to 2022). It covers fundamental concepts, recent approaches, applications, and trends, in addition to challenges and future research prospects in this domain area. Based on an analysis of 161 selected studies, we found that the state-of-the-art models employ deep learning to extract information from textual data. Finally, this study aimed to guide novice and experienced researchers in future research and serve as a foundation for this research area.

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“…Named Entity Recognition (NER) is used to extract structured information from unstructured text, such as Person, Location, Organization, etc [1]. NER plays an essential role in many downstream tasks of NLP, including information retrieval (IE) [2,3], question answering (QA) [4][5][6], entity-relationship extraction [7][8][9], knowledge graph construction [10,11], etc. In recent years, with the rapid development of artificial intelligence [12][13][14][15], CNER technology [16] has been widely used in general fields, such as network security field [17], social media field [18], medical field [19], etc.…”

Section: Introductionmentioning

confidence: 99%

MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition

2022

KSII TIIS

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Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

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A hybrid deep-learning approach for complex biochemical named entity recognition

Cited by 39 publications

References 35 publications

Legal Text Recognition Using LSTM-CRF Deep Learning Model

Legal Text Recognition Using LSTM-CRF Deep Learning Model

Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges

MSFM: Multi-view Semantic Feature Fusion Model for Chinese Named Entity Recognition

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