Computer Aided Writing — A Prototype of an Intelligent Word Processing System

2017 ◽  
Author(s):  
Andre Klahold ◽  
Johannes Zenkert ◽  
Madjid Fathi
Keyword(s):  
Word Processing ◽  
Processing System ◽  
Computer Aided

scholarly journals Can You Identify These Celebrities? A Network Analysis on Differences between Word and Face Recognition

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 699 ◽  
Author(s):  
Carmen Moret-Tatay ◽  
Inmaculada Baixauli-Fortea ◽  
M. Dolores Grau Sevilla ◽  
Tatiana Quarti Irigaray
Keyword(s):  
Face Recognition ◽  
Network Analysis ◽  
Word Processing ◽  
Processing System ◽  
Place Of Origin ◽  
Masked Repetition ◽  
The Relationship ◽  
Repetition Priming Effect ◽  
Vast Range

Face recognition is located in the fusiform gyrus, which is also related to other tasks such word recognition. Although these two processes have several similarities, there are remarkable differences that include a vast range of approaches, which results from different groups of participants. This research aims to examine how the word-processing system processes faces at different moments and vice versa. Two experiments were carried out. Experiment 1 allowed us to examine the classical discrimination task, while Experiment 2 allowed us to examine very early moments of discrimination. In the first experiment, 20 Spanish University students volunteered to participate. Secondly, a sample of 60 participants from different nationalities volunteered to take part in Experiment 2. Furthermore, the role of sex and place of origin were considered in Experiment 1. No differences between men and women were found in Experiment 1, nor between conditions. However, Experiment 2 depicted shorter latencies for faces than word names, as well as a higher masked repetition priming effect for word identities and word names preceded by faces. Emerging methodologies in the field might help us to better understand the relationship among these two processes. For this reason, a network analysis approach was carried out, depicting sub-communities of nodes related to face or word name recognition, which were replicated across different groups of participants. Bootstrap inferences are proposed to account for variability in estimating the probabilities in the current samples. This supports that both processes are related to early moments of recognition, and rather than being independent, they might be bilaterally distributed with some expert specializations or preferences.

Learning to use a word-processing system as a function of training strategy

1986 ◽  
Vol 5 (3) ◽  
pp. 203-216 ◽  
Author(s):  
SARA J. CZAJA ◽  
KATKA HAMMOND ◽  
JAMES J. BLASCOVICH ◽  
HELEN SWEDE
Keyword(s):  
Word Processing ◽  
Processing System ◽  
Training Strategy

Operator Corrections during Text Entry with Word Processing Systems

1982 ◽  
Vol 26 (7) ◽  
pp. 625-628 ◽  
Author(s):  
Alan S. Neal ◽  
William H. Emmons
Keyword(s):  
Error Correction ◽  
Error Detection ◽  
Word Processing ◽  
Processing System ◽  
Performance Data ◽  
Text Entry ◽  
System Data

In order to answer questions related to keying errors and operator corrections, performance data were collected on typists as they keyed text into a simulated word processing system. Data are presented on the frequency of error detection, the amount of time spent correcting errors, the number of characters erased per error correction, and the types of errors corrected. Comparisons are also made between operator corrected and uncorrected errors.

Aplikasi Tanya Jawab Otomatis Seputar Rekayasa Perangkat Lunak menggunakan Metode Cosine Similarity berbasis Android

Repositor ◽  
2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Rizky Heriawan Prayogo Tanjung ◽  
Maskur Maskur ◽  
Nur Hayatin
Keyword(s):  
Word Processing ◽  
Question Answering ◽  
Processing System ◽  
Cosine Similarity ◽  
Matching Method ◽  
Question Answering System ◽  
Question And Answer ◽  
Kappa Value ◽  
The Right

AbstrakJawaban pertanyaan aplikasi penjawab pertanyaan yang tersedia saat ini masih menggunakan metode pencocokan kata kunci untuk melakukan pencarian atas jawaban. Sistem penjawab pertanyaanotomatis adalah sistem yang secara otomatis mencoba menemukan kembali informasi yang benar untuk pertanyaan diajukan oleh user. Pertanyaan dapat dikembangkan untuk membantu dan membuat lebih mudah untuk menjawab pertanyaan tentang rekayasa perangkat lunak.Aplikasiini menggunakan metodeCosine Similarityyangmerupakan salah satu solusi untukmembantu mencari jawabanpertanyaanyang diinginkan dengantepat,yangbermanfaat untuk sistem pengolah kata. Karena dengan metode ini,tanya jawab otomatis dapat mencari data yang diinginkan oleh penanya,denganmenampilkan jawaban dengan bobot tertinggi sebagai jawaban yang paling tepat.Jawaban pertama atau bobot tertinggi yang dihasilkan oleh sistem adalah jawaban yang benar menurut penilaian sistem dan pakar.Jawaban pertama atau bobot tertinggi yang dihasilkan oleh sistem adalah jawaban yang benar menurut penilaian sistem, pakar dan pengujian Kappa.Hasil pengujian menggunakan kappa statistik memberikan nilai terbaik Kappa pada jawaban pertama (jawaban dengan bobot terbesar).Nilai tersebut membuktikan bahwa sistem yang telah dibangun dapat digunakan untuk mengetahui kemiripan antar kasus penggunaan pertanyaan dan jawaban.AbstractThe Answers of question answering applications that are available today are still using keyword matching method to perform a search for answering. Automatic question answering system is a automatically system used to find information that might correspond to the questions asked by the user. Questions can be developed to help and make it easier to answer questions about software engineering.This application uses the method of Cosine Similarity which is one solution to help searching for the desired answer of questions correctly, that is useful for word processing system. By this method, Automatic Question Answering can looking for desired data of user by showing the the highest weights answer as the best answer.The first or the highest answer resulted by system is the right answer based on system, expert and Kappa Testing. The result of Kappa testing giving the best Kappa value on the first answer (the highest weights answer). It proves that the system can be used to know the similarity between question and answer for between cases of using quetions and answers.

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