Enhancing Retrieval and Ranking Performance for Media Search Engine by Deep Learning

2016 ◽  
Author(s):  
Xugang Ye ◽  
Jingjing Li ◽  
Zijie Qi ◽  
Xiaodong He
Keyword(s):  
Deep Learning ◽  
Search Engine ◽  
Ranking Performance

scholarly journals Deep Learning Based Question Answering Search Engine

2021 ◽  
pp. 25-32
Author(s):  
Mrunal Malekar
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language ◽  
Search Engine ◽  
Language Processing ◽  
Question Answering ◽  
Research Work ◽  
Construction Company ◽  
Exact Answer ◽  
Search For Information

Domain based Question Answering is concerned with building systems which provide answers to natural language questions that are asked specific to a domain. It comes under Information Retrieval and Natural language processing. Using Information Retrieval, one can search for the relevant documents which may contain the answer but it won’t give the exact answer for the question asked. In the presented work, a question answering search engine has been developed which first finds out the relevant documents from a huge textual document data of a construction company and then goes a step beyond to extract answer from the extracted document. The robust question answering system developed uses Elastic Search for Information Retrieval [paragraphs extraction] and Deep Learning for answering the question from the short extracted paragraph. It leverages BERT Deep Learning Model to understand the layers and representations between the question and answer. The research work also focuses on how to improve the search accuracy of the Information Retrieval based Elastic Search engine which returns the relevant documents which may contain the answer.

scholarly journals NSGA-III-Based Deep-Learning Model for Biomedical Search Engines

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Manish Gupta ◽  
Naresh Kumar ◽  
Bhupesh Kumar Singh ◽  
Neha Gupta
Keyword(s):  
Deep Learning ◽  
Search Engine ◽  
Search Engines ◽  
Biomedical Imaging ◽  
Learning Model ◽  
Imaging Data ◽  
Testing Dataset ◽  
Proposed Model ◽  
Deep Learning Model ◽  
Nondominated Sorting

With the advancements in biomedical imaging applications, it becomes more important to provide potential results for searching the biomedical imaging data. During the health emergency, tremors require efficient results at rapid speed to provide results to spatial queries using the Web. An efficient biomedical search engine can obtain the significant search intention and return additional important contents in which users have already indicated some interest. The development of biomedical search engines is still an open area of research. Recently, many researchers have utilized various deep-learning models to improve the performance of biomedical search engines. However, the existing deep-learning-based biomedical search engines suffer from the overfitting and hyperparameter tuning problems. Therefore, in this paper, a nondominated-sorting-genetic-algorithm-III- (NSGA-III-) based deep-learning model is proposed for biomedical search engines. Initially, the hyperparameters of the proposed deep-learning model are obtained using the NSGA-III. Thereafter, the proposed deep-learning model is trained by using the tuned parameters. Finally, the proposed model is validated on the testing dataset. Comparative analysis reveals that the proposed model outperforms the competitive biomedical search engine models.

scholarly journals Improving Search Quality with Automatic Ranking Evaluation and Tuning

2020 ◽  
Author(s):  
Larícia Cavalcante ◽  
Ullayne Lima ◽  
Luciano Barbosa ◽  
Ana Luiza Gomes ◽  
Éden Santana ◽  
...  
Keyword(s):  
Search Engine ◽  
Optimization Algorithm ◽  
Experimental Evaluation ◽  
Evaluation System ◽  
Relevant Information ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm ◽  
Customer Support ◽  
Ranking Performance ◽  
Search Quality

Search is a common feature available in document-based applications. It allows users to find information of interest easier. Two essential aspects for building an effective search is to evaluate the ranking quality and be able to efficiently tune it based on this evaluation. In this paper, we present our Automatic Ranking Tuning and Evaluation System (ARTES) that measures the ranking performance based on users’ clicks on search resulting pages and automatically tunes the search ranking function by applying a Bayesian Optimization algorithm. Our system is integrated with Elasticsearch, a widely-used search engine, which provides the search functionality. The whole solution is currently used by our customer support platform to help users effectively find relevant information, as our experimental evaluation confirms.

scholarly journals Job Descriptions Keyword Extraction using Attention based Deep Learning Models with BERT

2021 ◽  
Author(s):  
Rishit Dagli ◽  
Ali Mustufa Shaikh ◽  
Hussain Mahdi ◽  
Sameer Nanivadekar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Search Engine ◽  
Keyword Extraction ◽  
Learning Models ◽  
Job Descriptions ◽  
Job Description ◽  
Learning Techniques ◽  
Related Text ◽  
Modeling Power

In this paper, we focus on creating a keywords extractor especially for a given job description job-related text corpus for better search engine optimization using attention based deep learning techniques. Millions of jobs are posted but most of them end up not being located due to improper SEO and keyword management. We aim to make this as easy to use as possible and allow us to use this for a large number of job descriptions very easily. We also make use of these algorithms to screen or get insights from large number of resumes, summarize and create keywords for a general piece of text or scientific articles. We also investigate the modeling power of BERT (Bidirectional Encoder Representations from Transformers) for the task of keyword extraction from job descriptions. We further validate our results by providing a fully-functional API and testing out the model with real-time job descriptions.

scholarly journals Job Descriptions Keyword Extraction using Attention based Deep Learning Models with BERT

2021 ◽  
Author(s):  
Rishit Dagli ◽  
Ali Mustufa Shaikh ◽  
Hussain Mahdi ◽  
Sameer Nanivadekar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Search Engine ◽  
Keyword Extraction ◽  
Learning Models ◽  
Job Descriptions ◽  
Job Description ◽  
Learning Techniques ◽  
Related Text ◽  
Modeling Power

In this paper, we focus on creating a keywords extractor especially for a given job description job-related text corpus for better search engine optimization using attention based deep learning techniques. Millions of jobs are posted but most of them end up not being located due to improper SEO and keyword management. We aim to make this as easy to use as possible and allow us to use this for a large number of job descriptions very easily. We also make use of these algorithms to screen or get insights from large number of resumes, summarize and create keywords for a general piece of text or scientific articles. We also investigate the modeling power of BERT (Bidirectional Encoder Representations from Transformers) for the task of keyword extraction from job descriptions. We further validate our results by providing a fully-functional API and testing out the model with real-time job descriptions.

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