Results 151 to 160 of about 17,118 (185)

Knowledge Graph-Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation.

J Med Internet Res
Xia Y, Li J, Deng B, Huang Q, Cai F, Xie Y, Sun X, Shi Q, Dan W, Zhan Y, Jiang L.   +10 more
europepmc   +1 more source

Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model

Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model
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Part-of-speech (POS) tagging using conditional random field (CRF) model for Khasi corpora

International Journal of Speech Technology, 2021
Khasi is a language that belongs to the Mon-Khmer language of the Austroasiatic group. Khasi language is spoken by the indigenous people of the state of Meghalaya in India. This paper presents a work on Part-of-speech (POS) tagging for the Khasi language by using the Conditional Random Field (CRF) method.
Sunita Warjri, Partha Pakray, Arnab Kumar Maji   +2 more
exaly   +2 more sources

Adopting Conditional Random Field (CRF) for Khasi Part-of-Speech Tagging (KPOST)

Lecture Notes in Networks and Systems, 2021
The main purpose of this paper is to develop part-of-speech (PoS) tagging for the Khasi language based on conditional random field (CRF) approaches. Khasi belongs to a Mon–Khmer language of the Austroasiatic language family that is spoken by the native people of the state Meghalaya, Northeastern Part of India.
Sunita Warjri, Partha Pakray, Arnab Kumar Maji   +2 more
exaly   +2 more sources

Aspect and Opinion Extraction of Indonesian Lipsticks Product Reviews using Conditional Random Field (CRF)

2021 13th International Conference on Knowledge and Smart Technology (KST), 2021
Aspect and opinion extraction is a key process in several downstream applications, such as market analysis. In this paper, we modeled aspect and opinion extraction of lipsticks product reviews in Indonesia using Conditional Random Field (CRF) method. The dataset contains review text written not only in standard and colloquial Indonesian languages but ...
Deffri Kun Indarta, Ade Romadhony
exaly   +2 more sources

Medical entity recognition using conditional random field (CRF)

2017 International Workshop on Big Data and Information Security (IWBIS), 2017
The main objective of this research is to extract the health information, such as diseases, symptoms, treatments and drugs from the health online forum discussion. The task is referred as the medical entity recognition (MER) in which is defined as the Named Entity Recognition (NER) task to extract the information from the unstructured text and ...
Raditya Herwando, Meganingrum Arista Jiwanggi, Mirna Adriani   +2 more
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CRF-TM: A Conditional Random Field Method for Predicting Transmembrane Topology

2015
Transmembrane proteins are important for cell transport biology and in the treatment of disease. Understanding the helix count and locations in transmembrane proteins is a key problem for structural and functional analyses. But there is a lack of high resolution three-dimensional structures.
Weizhong Lu, Baochuan Fu, Hongjie Wu, Qiang Lü, Kun Wang 0005, Min Jiang 0009   +5 more
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CRF-MEM: Conditional Random Field Model Based Modified Expectation Maximization Algorithm for Sarcasm Detection in Social Media

網際網路技術學刊, 2023
<p>Text processing is an important task in various machine learning applications. One among the applications is Sentiment analysis. However, the presence of sarcasm makes it difficult for analyzing the sentiment of the statement.
Anbarasu Sivalingam Anbarasu Sivalingam, Karthik Sundararajan Anbarasu Sivalingam, Anandhakumar Palanisamy Karthik Sundararajan   +2 more
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Part-of-speech tagging and parsing of Kannada text using Conditional Random Fields (CRFs)

2017 International Conference on Intelligent Computing and Control (I2C2), 2017
Parts of Speech tagging is consider as the second step in Natural Language Processing. In this paper we present Parts of Speech tagging and Chunking using Conditional Random Fields. We used Kannada corpus of 3000 sentences collected from newspaper. We train with 2500 sentences and tested with 500 sentences.
N M Suraksha, K Reshma, K M Shiva Kumar
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