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

2020 ◽  
Author(s):  
Bodhvi Gaur ◽  
Gurpreet Singh Saluja ◽  
Hamsa Bharathi Sivakumar ◽  
Sanjay Singh
Keyword(s):  
Neural Network ◽  
Seed Set ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Educational Institutions ◽  
Knowledge And Skills ◽  
Named Entity ◽  
Initial Seed ◽  
Machine Processing

Abstract 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.

scholarly journals Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition

2018 ◽  
Vol 10 (12) ◽  
pp. 123 ◽  
Author(s):  
Mohammed Ali ◽  
Guanzheng Tan ◽  
Aamir Hussain
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Word Embedding ◽  
Entity Recognition ◽  
Named Entity ◽  
Lstm Network

Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task of NLP and can be considered a classification problem. We propose a bidirectional long short-term memory (LSTM) model for this entity recognition task of the Arabic text. The LSTM network can process sequences and relate to each part of it, which makes it useful for the NER task. Moreover, we use pre-trained word embedding to train the inputs that are fed into the LSTM network. The proposed model is evaluated on a popular dataset called “ANERcorp.” Experimental results show that the model with word embedding achieves a high F-score measure of approximately 88.01%.

scholarly journals Tying of embeddings for improving regularization in neural networks for named entity recognition task

2018 ◽  
pp. 59-64
Author(s):  
M. Bevza
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech ◽  
Recent Developments ◽  
Knowledge Distillation

We analyze neural network architectures that yield state of the art results on named entity recognition task and propose a new architecture for improving results even further. We have analyzed a number of ideas and approaches that researchers have used to achieve state of the art results in a variety of NLP tasks. In this work, we present a few of them which we consider to be most likely to improve existing state of the art solutions for named entity recognition task. The architecture is inspired by recent developments in language modeling task. The suggested solution is based on a multi-task learning approach. We incorporate part of speech tags as input for the network. Part of speech tags to be yielded by some state of the art tagger and also ask the network to produce those tags in addition to the main named entity recognition tags. This way knowledge distillation from a strong part of speech tagger to our smaller network is happening. We hypothesize that designing neural network architecture in this way improves the generalizability of the system and provide arguments to support this statement.

SCIENTIFIC NAMED ENTITY RECOGNITION WITH THE HELP OF MODERN METHODS

2021 ◽  
Vol 75 (3) ◽  
pp. 94-99
Author(s):  
A.M. Yelenov ◽  
◽  
A.B. Jaxylykova ◽  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Support Vector ◽  
Scientific Article ◽  
Natural Languages ◽  
Named Entity ◽  
Learning Area

This research focuses on a comparative study of the Named Entity Recognition task for scientific article texts. Natural language processing could be considered as one of the cornerstones in the machine learning area which devotes its attention to the problems connected with the understanding of different natural languages and linguistic analysis. It was already shown that current deep learning techniques have a good performance and accuracy in such areas as image recognition, pattern recognition, computer vision, that could mean that such technology probably would be successful in the neuro-linguistic programming area too and lead to a dramatic increase on the research interest on this topic. For a very long time, quite trivial algorithms have been used in this area, such as support vector machines or various types of regression, basic encoding on text data was also used, which did not provide high results. The following dataset was used to process the experiment models: Dataset Scientific Entity Relation Core. The algorithms used were Long short-term memory, Random Forest Classifier with Conditional Random Fields, and Named-entity recognition with Bidirectional Encoder Representations from Transformers. In the findings, the metrics scores of all models were compared to each other to make a comparison. This research is devoted to the processing of scientific articles, concerning the machine learning area, because the subject is not investigated on enough properly level.The consideration of this task can help machines to understand natural languages better, so that they can solve other neuro-linguistic programming tasks better, enhancing scores in common sense.

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