Category Classification of the Training Set Combined with Sentence Multiplication for Semantic Data Extraction Using GENI Algorithm

Background: Increase in the internet data has increased the priority in the data extraction accuracy. Accuracy here lies with what data the user has requested for and what has been retrieved. The same large data sets that need to be analyzed make the required information retrieval a challenging task. Objective: To propose a new algorithm in an improved way than the traditional methods to classify the category or group to which each training sentence belongs. Method: Identifying the category to which the input sentence belongs is achieved by analyzing the Noun and Verb of each training sentence. NLP is applied to each training sentence and the group or category classification is achieved using the proposed GENI algorithm so that the classifier is trained efficiently to extract the user requested information. Results: The input sentences are transformed into a data table by applying GENI algorithm for group categorization. Plotting the graph in R tool, the accuracy of the group extracted by the Classifier involving GENI approach is higher than that of Naive Bayes & Decision Trees. Conclusion: It remains a challenging task to extract the user-requested data, when the user query is complex. Existing techniques are based more on the fixed attributes, and when we move with respect to the fixed attributes, it becomes too complex or impossible for us to determine the common group from the base sentence. Existing techniques are more suitable to a smaller dataset, whereas the proposed GENI algorithm does not hold any restrictions for the Group categorization of larger data sets.

WORD-FORMATION MEANS TEXTUAL COHESION AND COHERENCE

The article looks at word-formation means of textual cohesion and coherence. The analysis shows that the repetition of a suffix, prefix, confix or a base in adjacent or distant sentences in small textual segments can ensure the latter’s cohesion. The ‘source word – derivative’ pair has a considerable cohesive potential. It is especially typical of syntactic derivation. The nominalization of the verb – the predicate of the base sentence, its communicative nucleus – and its use in the next sentence, adjacent or distant one, indicates the theme and ensures the communicative prospects of a sentence within the context of the developing and unfolding text. Textual cohesion is provided by ‘source word – derivative’ word-formation pairs beyond syntactic derivation – when we deal with mutation and modification. The components of complex word-formation units – word-formation paradigms, word-formation families – can be used as cohesive devices because they share the same root morpheme, which is the bearer of general semantic meaning inherent in all members of a paradigmatic grouping.