Wow! eBook
Questions Similarity Detection Using Python and Web Application: UG and PG project with Source Code
Author(s): PARAMES JP (Author), MURALI DHARAN (Author)
- Publisher: Independently published
- Publication Date: March 1, 2022
- Language: English
- Print length: 72 pages
- ISBN-10: B09TMN8VHY
- ISBN-13: 9798424878350
Book Description
Questions Similarity Detection Using XGBoost Algorithm applications of natural language Processing presents a need for an effective method to compute the similarity between very short Texts or sentences. Another major Application of computing the sentence similarity can be to find the questions that have the same Semantic meanings and can be considered a duplicate. This gives rise to our project entitled Question Similarity Detection.
Question Similarity Detection is the system, which warns about the duplication of Questions. The main task of the project is to predict whether two questions can be Considered the same or Not. This shall help us identify whether a similar type of questions ever existed in the past, or not. The Project aims to fetch a question as an input, pair it with existing Questions, process them to gather their significant features, and finally put feed them to our Algorithm to make the final prediction. Some obvious text-based features are a number of words Matching between the two questions, length of the two questions, number of words, number of Characters, number of stop words. It can be done with Tf-IDF scores. The model exhibits high accuracy level of accuracy 82% here.
Question Similarity Detection is the system, which warns about the duplication of Questions. The main task of the project is to predict whether two questions can be Considered the same or Not. This shall help us identify whether a similar type of questions ever existed in the past, or not. The Project aims to fetch a question as an input, pair it with existing Questions, process them to gather their significant features, and finally put feed them to our Algorithm to make the final prediction. Some obvious text-based features are a number of words Matching between the two questions, length of the two questions, number of words, number of Characters, number of stop words. It can be done with Tf-IDF scores. The model exhibits high accuracy level of accuracy 82% here.
