A study on plagiarism detection and plagiarism direction identification using natural language processing techniques

Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to super cial, brute-force stringmatching techniques. If the text has undergone substantial semantic and syntactic changes, string-matching approaches do not perform well. In order to identify such changes, linguistic techniques which are able to perform a deeper analysis of the text are needed. To date, very limited research has been conducted on the topic of utilising linguistic techniques in plagiarism detection. This thesis provides novel perspectives on plagiarism detection and plagiarism direction identi cation tasks. The hypothesis is that original texts and rewritten texts exhibit signi cant but measurable di erences, and that these di erences can be captured through statistical and linguistic indicators. To investigate this hypothesis, four main research objectives are de ned. First, a novel framework for plagiarism detection is proposed. It involves the use of Natural Language Processing techniques, rather than only relying on the vii traditional string-matching approaches. The objective is to investigate and evaluate the in uence of text pre-processing, and statistical, shallow and deep linguistic techniques using a corpus-based approach. This is achieved by evaluating the techniques in two main experimental settings. Second, the role of machine learning in this novel framework is investigated. The objective is to determine whether the application of machine learning in the plagiarism detection task is helpful. This is achieved by comparing a thresholdsetting approach against a supervised machine learning classi er. Third, the prospect of applying the proposed framework in a large-scale scenario is explored. The objective is to investigate the scalability of the proposed framework and algorithms. This is achieved by experimenting with a large-scale corpus in three stages. The rst two stages are based on longer text lengths and the nal stage is based on segments of texts. Finally, the plagiarism direction identi cation problem is explored as supervised machine learning classi cation and ranking tasks. Statistical and linguistic features are investigated individually or in various combinations. The objective is to introduce a new perspective on the traditional brute-force pair-wise comparison of texts. Instead of comparing original texts against rewritten texts, features are drawn based on traits of texts to build a pattern for original and rewritten texts. Thus, the classi cation or ranking task is to t a piece of text into a pattern. The framework is tested by empirical experiments, and the results from initial experiments show that deep linguistic analysis contributes to solving the problems we address in this thesis. Further experiments show that combining shallow and viii deep techniques helps improve the classi cation of plagiarised texts by reducing the number of false negatives. In addition, the experiment on plagiarism direction detection shows that rewritten texts can be identi ed by statistical and linguistic traits. The conclusions of this study o er ideas for further research directions and potential applications to tackle the challenges that lie ahead in detecting text reuse.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Aplikasi Pendukung Desain Interior dengan Sistem Rekomendasi Berdasarkan Nama Brand Perabot Menggunakan Algoritma Content-Based Filtering Berbasis Web

Aplikasi perancangan desain interior dengan 3D Model memiliki kekurangan dalam memberikan panduan ide desain kepada pengguna baru dan tidak terdapat rekomendasi perabot yang dapat digunakan dalam desain berdasarkan selera pengguna. Sudah semestinya aplikasi 3D Model ruang menyediakan kemudahan bagi pengguna untuk mendapatkan ide desain dan rekomendasi perabot berdasarkan selera pengguna. Hal inilah yang melatarbelakangi penulis untuk memberikan solusi aplikasi pendukung desain interior. Penelitian ini bertujuan untuk membantu para pengguna agar menemukan ide desain ruangan yang sesuai dengan kebutuhan dan keinginan. Algoritma Text Preprocessing adalah tahapan untuk mempersiapkan teks menjadi data yang akan mengalami pengolahan pada tahapan berikutnya. Sistem rekomendasi Content-Based Filtering dengan algoritma Term Frequency ?¢â?¬â?? Inverse Document Frequency (TF-IDF) digunakan untuk memberikan bobot pada tiap kata yang terdapat pada nama item perabot. Algoritma Cosine Similarity diterapkan untuk menemukan kemiripan antara nama item perabot berdasarkan hasil pembobotan pada metode Term Frequency ?¢â?¬â?? Inverse Document Frequency (TF-IDF), pada hasil akhir diharapkan dapat membantu pengguna untuk mendapatkan hasil rekomendasi dari perabot yang disukai melalui ide desain perabot yang telah disediakan. Hasil pengujian dari algoritma ini adalah dengan menggunakan metode confusion matrix yang menunjukkan bahwa nilai precision 72%, recall 72%, accuracy 72%, dan error rate 51%, lalu berdasarkan hasil uji perangkat lunak menggunakan Black-Box Testing dapat ditarik kesimpulan bahwa perangkat lunak secara fungsional mengeluarkan hasil 96% sesuai dengan yang diharapkan