Natural Language Processing

This research paper presents a novel integrated framework for semantic text understanding that bridges traditional Natural Language Processing (NLP) techniques with modern deep learning approaches. We address semantic ambiguity by proposing a hybrid methodology that leverages statistical methods and neural architectures. Through experimentation on multiple datasets, the approach achieves a 15% improvement in semantic similarity tasks and a 12% enhancement in named entity recognition accuracy compared to baselines.

Keywords: semantic understanding · hybrid NLP · deep learning · statistical methods · natural language processing

This paper presents SAFE, a novel uniform approach to extract app features from single app pages, single reviews and to match them. We manually build 18 part-of-speech patterns and 5 sentence patterns that are frequently used in text referring to app features, then apply these patterns with several text pre- and post-processing steps. A major advantage is that it does not require large training and configuration data. For well-maintained app pages such as Google Drive the approach has a precision of 87% and on average 56% for 10 evaluated apps. SAFE also matches 87% of the features extracted from user reviews to those extracted from the app descriptions.

Keywords: User Reviews · App Store Analytics · Software Feature · Data Mining · NLP · Pattern Recognition