An ensemble deep learning model to enhance feature representation for entity detection

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Parsaeimehr Elham; Fartash Mehdi; Akbari Torkestani Javad

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Modeling in Engineering Year:2022 | Volume:20 | Issue:69 Page(s): 103-112

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

An ensemble deep learning model to enhance feature representation for entity detection

Author(s)

Parsaeimehr Elham | Fartash Mehdi | Akbari Torkestani Javad | Issue Writer Certificate

Keywords

Named Entity Recognition

LSTM

CNN

Word Embedding

Natural Language Processing

Abstract

One of the main processes in most Natural Language Processing (NLP), is Named Entity Recognition (NER). In this regard, some machine learning techniques have been presented that traditionally use manual features. Also, in recent years, deep neural network-based models have been proposed that achieve higher accuracy without relying on huge computations for feature engineering. Thus, in this article, we employ a combination of two deep learning models to capture the properties of the input sentence, including: long short term memory (LSTM) and convolutional neural network (CNN). In this architecture, extracting local features along with global features, more information is acquired for more accurate classification. We evaluate the performance of this architecture on two datasets CoNLL2003 and ACE05; and demonstrate that by adding a word level CNN, useful local properties are extracted that enhance the accuracy of the performance. Finally, we compare the performance of our system with competitors and our superiority is reported.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans