The trend in information retrieval systems is from document to sub-document retrieval, such as sentences in a summarization system and words or phrases in question-answering system. Despite this trend, systems continue to model language at a document level using the inverse document frequency (IDF). In this paper, we compare and contrast IDF with inverse sentence frequency (ISF) and inverse ter...

We propose a document retrieval that evaluates the degree of similarity between a query and a document in consideration of not only term-weights but also the amount of term frequencies. Different from tf-idf term-weighting schemes, the proposed scheme never reflects a term frequency in calculating the term-weight. We carried out an experiment in retrieval performance evaluation using a subset o...

Abstract The recent advent of data protection laws and regulations has emerged to protect privacy personal information individuals. As the cases breaches vulnerabilities are rapidly increasing, people aware more concerned about their privacy. These bring a significant attention software development teams address concerns in developing applications. today’s adopts an agile, issue-driven approach...

With the rapid development of internet technology, a large amount text data can be obtained. The classification (TC) technology plays very important role in processing massive data, but accuracy is directly affected by performance term weighting TC. Due to original design information retrieval (IR), frequency-inverse document frequency (TF-IDF) not effective enough for TC, especially with unbal...

A dialogue system is a software program that enables a user to interact with a computer using a natural language (Kang et al. 2014). Since an essential task of the dialogue system is to understand what the user says, it must be able to determine the user’s intention indicated in the user’s utterance. A speech-act is a linguistic action and implies the user’s intention. Therefore, the dialogue s...

Sentiment analysis can detect hate speech using the Natural Language Processing (NLP) concept. This process requires annotation of text in labeling. However, when carried out by people, this must use experts field speech, so there is no subjectivity. In addition, if processed humans, it will take a long time and allow errors for extensive data. To solve problem, we propose an automatic with con...

With publicly-available data collected from mainstream information platforms, this study used the term frequency inverse document (TF-IDF) algorithm to detect 74 popular terms and phrases about employment, analyzed changes in ranking of these phrases, visualized changing trend attention employment skills 2017 2019. The research result will facilitate application big technology teaching administ...

Text Categorization is traditionally done by using the term frequency and inverse document frequency.This type of method is not very good because, some words which are not so important may appear in the document .The term frequency of unimportant words may increase and document may be classified in the wrong category.For reducing the error of classifying of documents in wrong category. The Dist...

In this study, it is aimed to predict the data obtained from answers given by students who receive programming education open-ended questions with text mining algorithms. Thus, text-based on computational identity and empowement were analyzed performances of different algorithms compared. The participants research consisted 646 whose age range was between 12-20 received education. An electronic...

Many microbes can acquire genetic material from their environment and incorporate it into their genome, a process known as lateral genetic transfer (LGT). Computational approaches have been developed to detect genomic regions of lateral origin, but typically lack sensitivity, ability to distinguish donor from recipient, and scalability to very large datasets. To address these issues we have int...