Adapting Machine Learning Techniques for Developing Automatic Q&A Interaction Module for Translation Robots based on NLP

Research on Automatic Q&A Interaction Module of Computer-based Translation Robot is a study that focuses on developing an automatic question and answer (Q&A) interaction module for computer-based translation robots. The goal of the research is to enhance the capability of translation robots to perform more human-like interactions with users, particularly in terms of providing more efficient and accurate translations. In this paper proposed a Conditional Random Field Discriminative Analysis (CRFDA) for feature extraction to derive translation robot with Q&A. The proposed CRFDA model comprises of the discriminative analysis for the CRF model. The estimation CRF model uses the bi-directional classifier for the estimation of the feature vector. Finally, the classification is performed with the voting-based classification model for feature extraction. The performance of the CRFDA model is examined based on the Name Entity (Nes) in the TempVal1 &2 dataset. The extraction is based on the strict and relaxed feature model for the exact match and slight variation. The simulation analysis expressed that proposed CRFDA model achieves a classification accuracy of 91% which is significantly higher than the state-of-art techniques

A Surveyon Detection of Reviews Using Sentiment Classification of Methods

Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e - commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. As the numbers of customers are growin g, reviews received by products are also growing in large amount. Thus, mining opinions from product reviews is an important research topic. In the fast decade considerable research has been done i n academia. However, existing research is more focused towa rds categorization and summary of such online opinions. In this paper we survey various techniques to classify opinion as positive or negative and also detection of reviews as spam or non - spam