scholarly journals Domain-Specific Chinese Word Segmentation Based on Bi-Directional Long-Short Term Memory Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12993-13002 ◽  
Author(s):  
Dangguo Shao ◽  
Na Zheng ◽  
Zhaoqiang Yang ◽  
Zhenhua Chen ◽  
Yan Xiang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Word Segmentation ◽  
Memory Model ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Short Term ◽  
Term Memory ◽  
Domain Specific ◽  
Long Short Term Memory

scholarly journals Switch-LSTMs for Multi-Criteria Chinese Word Segmentation

2019 ◽  
Vol 33 ◽  
pp. 6457-6464 ◽  
Author(s):  
Jingjing Gong ◽  
Xinchi Chen ◽  
Tao Gui ◽  
Xipeng Qiu
Keyword(s):  
Short Term Memory ◽  
Word Segmentation ◽  
Previous Method ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Short Term ◽  
Term Memory ◽  
Criterion Learning ◽  
Long Short Term Memory ◽  
New Criteria

Multi-criteria Chinese word segmentation is a promising but challenging task, which exploits several different segmentation criteria and mines their common underlying knowledge. In this paper, we propose a flexible multi-criteria learning for Chinese word segmentation. Usually, a segmentation criterion could be decomposed into multiple sub-criteria, which are shareable with other segmentation criteria. The process of word segmentation is a routing among these sub-criteria. From this perspective, we present Switch-LSTMs to segment words, which consist of several long short-term memory neural networks (LSTM), and a switcher to automatically switch the routing among these LSTMs. With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria. Experiments show that our model obtains significant improvements on eight corpora with heterogeneous segmentation criteria, compared to the previous method and single-criterion learning.

scholarly journals Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables

2021 ◽  
Vol 35 (4) ◽  
pp. 1167-1181
Author(s):  
Yun Bai ◽  
Nejc Bezak ◽  
Bo Zeng ◽  
Chuan Li ◽  
Klaudija Sapač ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Runoff Forecasting ◽  
Long Short Term Memory

scholarly journals BO-LSTM: Classifying relations via long short-term memory networks along biomedical ontologies

2018 ◽  
Author(s):  
Andre Lamurias ◽  
Luka A. Clarke ◽  
Francisco M. Couto
Keyword(s):  
Deep Learning ◽  
Text Mining ◽  
Drug Interactions ◽  
Short Term Memory ◽  
Biomedical Ontologies ◽  
Short Term ◽  
Term Memory ◽  
Domain Specific ◽  
Learning Techniques ◽  
Long Short Term Memory

AbstractRecent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.We propose a new model, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using an open biomedical ontology, which in our case-study was Chemical Entities of Biological Interest (ChEBI). We assessed the performance of BO-LSTM on detecting and classifying drug-drug interactions in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved both the F1-score of the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. Our findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.Author summaryA high quantity of biomedical information is only available in documents such as scientific articles and patents. Due to the rate at which new documents are produced, we need automatic methods to extract useful information from them. Text mining is a subfield of information retrieval which aims at extracting relevant information from text. Scientific literature is a challenge to text mining because of the complexity and specificity of the topics approached. In recent years, deep learning has obtained promising results in various text mining tasks by exploring large datasets. On the other hand, ontologies provide a detailed and sound representation of a domain and have been developed to diverse biomedical domains. We propose a model that combines deep learning algorithms with biomedical ontologies to identify relations between concepts in text. We demonstrate the potential of this model to extract drug-drug interactions from abstracts and drug descriptions. This model can be applied to other biomedical domains using an annotated corpus of documents and an ontology related to that domain to train a new classifier.

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