Conditional Random Fields for Pattern Recognition Applied  to Structured Data

Burr, Tom; Skurikhin, Alexei N.

doi:10.3390/a8030466

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…It is known that CRF performs better with structured data [5], and since resumes are semi-structured documents, we have experimented with the CRF layer, and the results are given in Table 6. The results obtained through our proposed model are given in Table 7.…”

Section: Resultsmentioning

confidence: 99%

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Gaur

Saluja

Sivakumar

et al. 2020

Neural Comput & Applic

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A job seeker’s resume contains several sections, including educational qualifications. Educational qualifications capture the knowledge and skills relevant to the job. Machine processing of the education sections of resumes has been a difficult task. In this paper, we attempt to identify educational institutions’ names and degrees from a resume’s education section. Usually, a significant amount of annotated data is required for neural network-based named entity recognition techniques. A semi-supervised approach is used to overcome the lack of large annotated data. We trained a deep neural network model on an initial (seed) set of resume education sections. This model is used to predict entities of unlabeled education sections and is rectified using a correction module. The education sections containing the rectified entities are augmented to the seed set. The updated seed set is used for retraining, leading to better accuracy than the previously trained model. This way, it can provide a high overall accuracy without the need of large annotated data. Our model has achieved an accuracy of 92.06% on the named entity recognition task.

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Section: Resultsmentioning

confidence: 99%

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Gaur

Saluja

Sivakumar

et al. 2020

Neural Comput & Applic

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Neural Network Optimization Method and Its Application in Information Processing

Wang

Fan

2021

Mathematical Problems in Engineering

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Neural network theory is the basis of massive information parallel processing and large-scale parallel computing. Neural network is not only a highly nonlinear dynamic system but also an adaptive organization system, which can be used to describe the intelligent behavior of cognition, decision-making, and control. The purpose of this paper is to explore the optimization method of neural network and its application in information processing. This paper uses the characteristic of SOM feature map neural network to preserve the topological order to estimate the direction of arrival of the array signal. For the estimation of the direction of arrival of single-source signals in array signal processing, this paper establishes a uniform linear array and arbitrary array models based on the distance difference vector to detect DOA. The relationship between the DDOA vector and the direction of arrival angle is regarded as a mapping from the DDOA space to the AOA space. For this mapping, through derivation and analysis, it is found that there is a similar topological distribution between the two variables of the sampled signal. In this paper, the network is trained by uniformly distributed simulated source signals, and then the trained network is used to perform AOA estimation effect tests on simulated noiseless signals, simulated Gaussian noise signals, and measured signals of sound sources in the lake. Neural network and multisignal classification algorithms are compared. This paper proposes a DOA estimation method using two-layer SOM neural network and theoretically verifies the reliability of the method. Experimental research shows that when the signal-to-noise ratio drops from 20 dB to 1 dB in the experiment with Gaussian noise, the absolute error of the AOA prediction is small and the fluctuation is not large, indicating that the prediction effect of the SOM network optimization method established in this paper does not vary. The signal-to-noise ratio drops and decreases, and it has a strong ability to adapt to noise.

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Conditional Random Fields for Pattern Recognition Applied to Structured Data

Cited by 2 publications

References 57 publications

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Semi-supervised deep learning based named entity recognition model to parse education section of resumes

Neural Network Optimization Method and Its Application in Information Processing

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