Initialism disambiguation: Man versus machine

Abstract

Disambiguation of ambiguous initialisms and acronymsis critical to the proper understanding of various types of texts. A model that attempts to solve this has previously been presented. This model contained various baseline features, including contextual relationship features, statistical features, and language-specific features. The domain of Jewish law documents written in Hebrew and Aramaic is known to be rich in ambiguous abbreviations and therefore this model was implemented and applied over 2 separate corpuses within this domain. Several common machine-learning (ML) methods were tested with the intent of finding a successful integration of the baseline feature variants. When the features were evaluated individually, the best averaged results were achieved by a library for support vector machines (LIBSVM); 98.07% of the ambiguous abbreviations, which were researched in the domain, were disambiguated correctly. When all the features were evaluated together, the J48 ML method achieved the best result, with 96.95% accuracy. In this paper, we examine the system's degree of success and the degree of its professionalism by conducting a comparison between this system's results and the results achieved by 39 participants, highly fluent in the research domain. Despite the fact that all the participants had backgrounds in religious scriptures and continue to study these texts, the system's accuracy rate, 98.07%, was significantly higher than the average accuracy result of the participants, 91.65%. Further analysis of the results for each corpus implies that participants overcomplicate the required task, as well as exclude vital information needed to properly examine the context of a given initialism.

Extraction of time-related expressions using text mining with application to Hebrew

“…The third difference is that in Rabbinic documents, which are written in Hebrew and Aramaic, there is much more frequent use of abbreviations and acronyms than in regular Hebrew documents [15]. A study conducted by [16] demonstrated that the manual disambiguation of an acronym is an extremely time-consuming process, and it is a very difficult task even for a professional. For these reasons, keywords and keyphrases that appear in rabbinic responsa documents are much more difficult to extract than keywords and keyphrases in academic papers or any other English article.…”

From an Artificial Neural Network to Teaching

“…HaCohen-Kerner et al (2004) show that there are 40,000 abbreviations in Hebrew, compared to 17,000 in English. A study made in 2013 (HaCohen-Kerner et al, 2013) shows that the manual disambiguation of an acronym is a very time-consuming process, and it is a very difficult task even for a professional; (5) Hebrew is an ancient language; people have been speaking that language since the beginning of the 2nd millennium BCE. In Hebrew, there are written texts from two thousand years ago and more, most of them rabbinical (religious) texts (Mughaz, 2003;Mughaz et al, 2019aMughaz et al, , 2019b.…”