1 دانشگاه شهید چمران اهواز؛ اهواز؛ ایران.
2 دانشگاه شیراز؛ شیراز؛ ایران.
3 دانشگاه علوم پزشکی بوشهر؛ بوشهر؛ ایران.
عنوان مقاله [English]
In this research a new algorithm for facet extraction has been developed and introduced, which provides the experimental possibility of identifying facets based on a literary warrant. In the field of automatic facet extraction two main ideas were considered by reviewing the researches. The first idea is that the facet appears in the context. Therefore, to identify the facet in a corpus, its context must be examined. The second idea is that the facet is the focal point in a lexical tree that is neither very general nor very specific.
Based on these two ideas, first, the corpus in the medicine area and the obstetrics and gynaecology domain was prepared. The research team selected three corpora from the literary warrant and used the abstract and title of the collection of articles in top 20 journals of the field to create a contextual corpus. This collection contained 167071 documents. 2000 articles were randomly selected to create the origin corpus. The third body is the lexical corpus. The proper words of the corpus were extracted using a web-based service. The output contained 514 words. Duplicate words were removed and finally, 480 important words were identified. Then, the words were expanded in the contextual corpus with the help of the supervisor (Mesh) and then-candidate dissertations were extracted based on the two conditions of frequency-based Shifting and rank-based Shifting. Finally, using the three rules of specificity, substitution, and generality, the identified facets were modified and named. Finally, 26 facets were identified in the domain of gynaecology and obstetrics. Comparing the proposed algorithm with other algorithms, it was found that the combination of statistical approach and tree pruning can have better results than purely statistical approach or tree pruning. Also, the comparison of the output facets of the algorithm with the traditional facets in this obstetrics and gynaecology domain showed that the output of the algorithm is smaller and more useful for browsing information retrieval tools. Also, in this study was specified that specialized domain facets are different from general facets and can be redefined independently, but the results cannot be generalized to all medical domains and other researches are needed to be done in other fields.