Application of Natural Language Processing and Evidential Analysis to Web-Based Intelligence Information Acquisition

The quality of decisions made in business and government relates directly to the quality of the information used to formulate the decision. This information may be retrieved from an organization's knowledge base (Intranet) or from the World Wide Web. Intelligence services Intranet held information can be efficiently manipulated by technologies based upon either semantics such as ontologies, or statistics such as meaning-based computing. These technologies require complex processing of large amount of textual information. However, they cannot currently be effectively applied to Web-based search due to various obstacles, such as lack of semantic tagging. A new approach proposed in this paper supports Web-based search for intelligence information utilizing evidence-based natural language processing (NLP). This approach combines traditional NLP methods for filtering of Web-search results, Grounded Theory to test the completeness of the evidence, and Evidential Analysis to test the quality of gathered information. The enriched information derived from the Web-search will be transferred to the intelligence services knowledge base for handling by an effective Intranet search system thus increasing substantially the information for intelligence analysis. The paper will show that the quality of retrieved information is significantly enhanced by the discovery of previously unknown facts derived from known facts

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

Named Entity Recognition for English Language Using Deep Learning Based Bi Directional LSTM-RNN

The NER has been important in different applications like data Retrieval and Extraction, Text Summarization, Machine Translation, Question Answering (Q-A), etc. While several investigations have been carried out for NER in English, a high-accuracy tool still must be designed per the Literature Survey. This paper suggests an English Named Entities Recognition methodology using NLP algorithms called Bi-Directional Long short-term memory-based recurrent neural network (LSTM-RNN). Most English Language NER systems use detailed features and handcrafted algorithms with gazetteers. The proposed model is language-independent and has no domain-specific features or handcrafted algorithms. Also, it depends on semantic knowledge from word vectors realized by an unsupervised learning algorithm on an unannotated corpus. It achieved state-of-the-art performance in English without the use of any morphological research or without using gazetteers of any sort. A little database group of 200 sentences includes 3080 words. The features selection and generations are presented to catch the Name Entity. The proposed work is desired to forecast the Name Entity of the focus words in a sentence with high accuracy with the benefit of practical knowledge acquisition techniques