All issues

text-align: justify;">Events and character extraction from narratives is a fundamental task in text analysis. The application of event extraction techniques ranges from the summarization of different documents to the analysis of medical notes. We identify events based on a framework named “four W” (Who, What, When, Where) to capture all the essential components like the actors, actions, time, and places. In this paper, we explore two prominent techniques for event extraction: statistical parsing of syntactic trees and semantic role labeling. While these techniques were investigated by different researchers in isolation, we directly compare the performance of the two approaches on our custom dataset, which we have annotated.

text-align: justify;">Our analysis shows that statistical parsing of syntactic trees outperforms semantic role labeling in event and character extraction, especially in identifying specific details. Nevertheless, semantic role labeling demonstrate good performance in correct actor identification. We evaluate the effectiveness of both approaches by comparing different metrics like precision, recall, and F1-scores, thus, demonstrating their respective advantages and limitations.

text-align: justify;">Moreover, as a part of our work, we propose different future applications of event extraction techniques that we plan to investigate. The areas where we want to apply these techniques include code analysis and source code authorship attribution. We consider using event extraction to retrieve key code elements as variable assignments and function calls, which can further help us to analyze the behavior of programs and identify the project’s contributors. Our work provides novel understandings of the performance and efficiency of statistical parsing and semantic role labeling techniques, offering researchers new directions for the application of these techniques.

A new method of statistical analysis of texts is suggested. The frequency distribution of the first significant digits in numerals of English-language texts is considered. We have taken into account cardinal as well as ordinal numerals expressed both in figures, and verbally. To identify the author’s use of numerals, we previously deleted from the text all idiomatic expressions and set phrases accidentally containing numerals, as well as itemizations and page numbers, etc. Benford’s law is found to hold approximately for the frequencies of various first significant digits of compound literary texts by different authors; a marked predominance of the digit 1 is observed. In coherent authorial texts, characteristic deviations from Benford’s law arise which are statistically stable significant author peculiarities that allow, under certain conditions, to consider the problem of authorship and distinguish between texts by different authors. The text should be large enough (at least about 200 kB). At the end of $\{1, 2, \ldots, 9\}$ digits row, the frequency distribution is subject to strong fluctuations and thus unrepresentative for our purpose. The aim of the theoretical explanation of the observed empirical regularity is not intended, which, however, does not preclude the applicability of the proposed methodology for text attribution. The approach suggested and the conclusions are backed by the examples of the computer analysis of works by W.M. Thackeray, M. Twain, R. L. Stevenson, J. Joyce, sisters Bront¨e, and J.Austen. On the basis of technique suggested, we examined the authorship of a text earlier ascribed to L. F. Baum (the result agrees with that obtained by different means). We have shown that the authorship of Harper Lee’s “To Kill a Mockingbird” pertains to her, whereas the primary draft, “Go Set a Watchman”, seems to have been written in collaboration with Truman Capote. All results are confirmed on the basis of parametric Pearson’s chi-squared test as well as non-parametric Mann –Whitney U test and Kruskal –Wallis test.