Search (3 results, page 1 of 1)

  • × author_ss:"HaCohen-Kerner, Y."
  • × year_i:[2010 TO 2020}
  1. HaCohen-Kerner, Y.; Kass, A.; Peretz, A.: HAADS: a Hebrew Aramaic abbreviation disambiguation system (2010) 0.00 
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    Abstract
    In many languages abbreviations are very common and are widely used in both written and spoken language. However, they are not always explicitly defined and in many cases they are ambiguous. This research presents a process that attempts to solve the problem of abbreviation ambiguity using modern machine learning (ML) techniques. Various baseline features are explored, including context-related methods and statistical methods. The application domain is Jewish Law documents written in Hebrew and Aramaic, which are known to be rich in ambiguous abbreviations. Two research approaches were implemented and tested: general and individual. Our system applied four common ML methods to find a successful integration of the various baseline features. The best result was achieved by the SVM ML method in the individual research, with 98.07% accuracy.
    Type
    a
  2. HaCohen-Kerner, Y.; Kass, A.; Peretz, A.: Initialism disambiguation : man versus machine (2013) 0.00 
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    Abstract
    Disambiguation of ambiguous initialisms and acronyms is 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.
    Type
    a
  3. HaCohen-Kerner, Y.; Beck, H.; Yehudai, E.; Rosenstein, M.; Mughaz, D.: Cuisine : classification using stylistic feature sets and/or name-based feature sets (2010) 0.00 
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    Abstract
    Document classification presents challenges due to the large number of features, their dependencies, and the large number of training documents. In this research, we investigated the use of six stylistic feature sets (including 42 features) and/or six name-based feature sets (including 234 features) for various combinations of the following classification tasks: ethnic groups of the authors and/or periods of time when the documents were written and/or places where the documents were written. The investigated corpus contains Jewish Law articles written in Hebrew-Aramaic, which present interesting problems for classification. Our system CUISINE (Classification UsIng Stylistic feature sets and/or NamE-based feature sets) achieves accuracy results between 90.71 to 98.99% for the seven classification experiments (ethnicity, time, place, ethnicity&time, ethnicity&place, time&place, ethnicity&time&place). For the first six tasks, the stylistic feature sets in general and the quantitative feature set in particular are enough for excellent classification results. In contrast, the name-based feature sets are rather poor for these tasks. However, for the most complex task (ethnicity&time&place), a hill-climbing model using all feature sets succeeds in significantly improving the classification results. Most of the stylistic features (34 of 42) are language-independent and domain-independent. These features might be useful to the community at large, at least for rather simple tasks.
    Type
    a

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