scholarly journals Predicting Ranking for Scientific Research Papers using Scalable Tensor Flow Library and Learning to Rank

2020 ◽  
Vol 9 (4) ◽  
pp. 2183-2188
Keyword(s):  
Information Retrieval ◽  
Supervised Learning ◽  
Language Processing ◽  
Traditional Method ◽  
Learning To Rank ◽  
New Technology ◽  
Scientific Research ◽  
Vital Role ◽  
Relative Order ◽  
Research Papers

Scientific research papers play a vital role for innovation of new technology. It is the future of the development where a novice person can understand the technology and tries to develop a new idea. In this paper, concentrated on relative order for a group of items applied to scientific research paper. In this process we identify how LTR differs from standard supervised learning in the sense that instead of looking at a precise score or class for each sample, it aims to discover the best relative order for a group of items. Firstly we identified the work of ranking of scientific research papers using traditional method know as supervised learning. Secondly we evaluated and made the comparison between the supervised learning and the scalable Tensor flow library for learning to rank. Apart from solving information retrieval problems, Learning to Ranking is mostly used in areas like Natural language processing (NLP), Machine translation, Computational biology or Sentiment analysis.

scholarly journals Predicting rank for scientific research papers using supervised learning

2019 ◽  
Vol 15 (2) ◽  
pp. 182-190 ◽  
Author(s):  
Mohamed El Mohadab ◽  
Belaid Bouikhalene ◽  
Said Safi
Keyword(s):  
Supervised Learning ◽  
Scientific Research ◽  
Research Papers

Libraries and information provision for African relief

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Elsemieke Wishart
Keyword(s):  
New Technology ◽  
Information Provision ◽  
Modern Technology ◽  
Vital Role ◽  
Sub Saharan Africa ◽  
Research Papers ◽  
Research Article ◽  
Poverty Relief ◽  
Community Information ◽  
Sub Saharan

This research article explores the concept that libraries as communication centres in sub-Saharan Africa can play a vital role in bringing poverty relief by providing greater access to information through modern technology. It discusses the patrons who can benefit from community information centres and explores their particular needs. I have researched how modern tools like the Internet, computers, e-readers and cell phones can bring valuable information to impoverished citizens. My research was conducted through reading research papers using article databases, books and Internet websites. The future for libraries in sub-Saharan Africa is bright, as new technology opens up vast opportunities to share information in a way that is accessible, affordable and adaptable to the needs of the African people. I recommend that librarians and relief organisations in Southern Africa seriously consider using modern technology to provide information that will empower its citizens.

Rancang Bangun Aplikasi Chatbot Sebagai Media Pencarian Informasi Anime Menggunakan Regular Expression Pattern Matching

2017 ◽  
Vol 9 (1) ◽  
pp. 19-24 ◽  
Author(s):  
David Domarco ◽  
Ni Made Satvika Iswari
Keyword(s):  
Information Retrieval ◽  
Expression Pattern ◽  
Pattern Matching ◽  
Language Processing ◽  
Regular Expression ◽  
Technology Development ◽  
Data Retrieval ◽  
Index Terms ◽  
Retrieval Engine ◽  
Behavioral Intention To Use

Technology development has affected many areas of life, especially the entertainment field. One of the fastest growing entertainment industry is anime. Anime has evolved as a trend and a hobby, especially for the population in the regions of Asia. The number of anime fans grow every year and trying to dig up as much information about their favorite anime. Therefore, a chatbot application was developed in this study as anime information retrieval media using regular expression pattern matching method. This application is intended to facilitate the anime fans in searching for information about the anime they like. By using this application, user can gain a convenience and interactive anime data retrieval that can’t be found when searching for information via search engines. Chatbot application has successfully met the standards of information retrieval engine with a very good results, the value of 72% precision and 100% recall showing the harmonic mean of 83.7%. As the application of hedonic, chatbot already influencing Behavioral Intention to Use by 83% and Immersion by 82%. Index Terms—anime, chatbot, information retrieval, Natural Language Processing (NLP), Regular Expression Pattern Matching

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

2020 ◽  
Vol 01 ◽  
Author(s):  
Radha Guha
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Text Summarization ◽  
The Internet ◽  
Specific Domain ◽  
User Query ◽  
College Information ◽  
Chat Bot

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

Adversarial training for few-shot text classification

2021 ◽  
Vol 14 (2) ◽  
pp. 201-214
Author(s):  
Danilo Croce ◽  
Giuseppe Castellucci ◽  
Roberto Basili
Keyword(s):  
Supervised Learning ◽  
Language Processing ◽  
Reproducing Kernel ◽  
Generative Adversarial Networks ◽  
Training Material ◽  
Semantic Classification ◽  
Universal Sentence ◽  
Kernel Hilbert Spaces ◽  
Supervised Methods ◽  
Low Dimensional

In recent years, Deep Learning methods have become very popular in classification tasks for Natural Language Processing (NLP); this is mainly due to their ability to reach high performances by relying on very simple input representations, i.e., raw tokens. One of the drawbacks of deep architectures is the large amount of annotated data required for an effective training. Usually, in Machine Learning this problem is mitigated by the usage of semi-supervised methods or, more recently, by using Transfer Learning, in the context of deep architectures. One recent promising method to enable semi-supervised learning in deep architectures has been formalized within Semi-Supervised Generative Adversarial Networks (SS-GANs) in the context of Computer Vision. In this paper, we adopt the SS-GAN framework to enable semi-supervised learning in the context of NLP. We demonstrate how an SS-GAN can boost the performances of simple architectures when operating in expressive low-dimensional embeddings; these are derived by combining the unsupervised approximation of linguistic Reproducing Kernel Hilbert Spaces and the so-called Universal Sentence Encoders. We experimentally evaluate the proposed approach over a semantic classification task, i.e., Question Classification, by considering different sizes of training material and different numbers of target classes. By applying such adversarial schema to a simple Multi-Layer Perceptron, a classifier trained over a subset derived from 1% of the original training material achieves 92% of accuracy. Moreover, when considering a complex classification schema, e.g., involving 50 classes, the proposed method outperforms state-of-the-art alternatives such as BERT.

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