A Combinatorial Optimized Knapsack Linear Space for Information Retrieval

Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data analysis. Currently, the classifiers are used to maximize the performance of web-page recommendation in terms of precision and satisfaction. The recent method disambiguates contextual sentiment using conceptual prediction with robustness, however the conceptual prediction method is not able to yield the optimal solution. Context-dependent terms are primarily evaluated by constructing linear space of context features, presuming that if the terms come together in certain consumer-related reviews, they are semantically reliant. Moreover, the more frequently they coexist, the greater the semantic dependency is. However, the influence of the terms that coexist with each other can be part of the frequency of the terms of their semantic dependence, as they are non-integrative and their individual meaning cannot be derived. In this work, we consider the strength of a term and the influence of a term as a combinatorial optimization, called Combinatorial Optimized Linear Space Knapsack for Information Retrieval (COLSK-IR). The COLSK-IR is considered as a knapsack problem with the total weight being the “term influence” or “influence of term” and the total value being the “term frequency” or “frequency of term” for semantic data analysis. The method, by which the term influence and the term frequency are considered to identify the optimal solutions, is called combinatorial optimizations. Thus, we choose the knapsack for performing an integer programming problem and perform multiple experiments using the linear space through combinatorial optimization to identify the possible optimum solutions. It is evident from our experimental results that the COLSK-IR provides better results than previous methods to detect strongly dependent snippets with minimum ambiguity that are related to inter-sentential context during semantic data analysis.