A Hybrid Ensemble Word Embedding based Classification Model for Multi-document Summarization Process on Large Multi-domain Document Sets

Abstract

Contextual text feature extraction and classification play a vital role in the multi-document summarization process. Natural language processing (NLP) is one of the essential text mining tools which is used to preprocess and analyze the large document sets. Most of the conventional single document feature extraction measures are independent of contextual relationships among the different contextual feature sets for the document categorization process. Also, these conventional word embedding models such as TF-ID, ITF-ID and Glove are difficult to integrate into the multi-domain feature extraction and classification process due to a high misclassification rate and large candidate sets. To address these concerns, an advanced multi-document summarization framework was developed and tested on number of large training datasets. In this work, a hybrid multi-domain glove word embedding model, multi-document clustering and classification model were implemented to improve the multi-document summarization process for multi-domain document sets.Experimental results prove that the proposed multi-document summarization approach has improved efficiency in terms of accuracy, precision, recall, F-score and run time (ms) than the existing models.

Word by Word Labelling of Romanized Sindhi Text by using Online Python Tool

“…This model combines the use of online and manual approach. This type of model is commonly used to create rules for language analysis and is a popular NLP technique to perform different tasks on different languages as it is easier to understand while the results are based on ground truth values [19][20]. Fig.…”

Hybrid Approach Used to Analyze the Sentiments of Romanized Text (Sindhi)

“…Sentiment analysis is the analysis of opinions about users [3,4]. The principle part of artificial intelligence (AI) and man-made brainpower in NLP is to measure the content and investigate the importance of the content [5]. The information or text utilized for the Natural Language Processing looks like unstructured and organized information or text [6].…”