Disambiguation for Arabic QuestionAnswering System. Because of the increasing amounts of Arabic content on the Internet and the increasing demand for information, Arabic question answering (QA) systems are gaining great importance. Nevertheless, automatic answering of questions in natural language is one of natural language processingxe2x80x99s most challenging tasks. In this paper, we address the issue of processing Arabic Question Answering in the medical domain where there are several specific challenges. The main challenge in dealing with medical field in Arabic language is the need to resolve ambiguity. This issue, though, was not thoroughly studied in related works. Therefore, our QA system requires disambiguation solution to select the correct meaning in order to return the correct answer. The goal of this work is to resolve Arabicrelated ambiguities as well as medicalrelated ambiguities. To achieve this goal, we use dictionaries and transducers using NooJ platform to answer any factoid or complex medical question. Experimentations of the disambiguation task of our Arabic medical question answering system show interesting results.

Knowledge Abstraction Matching for Medical Question Answering Medical Question Answering (medical QA), which studies the problem of automatically answering patientsu0027 medical questions online, is one of the major applications of bioinformatics. Though many efforts have been made before, the medical QA system still deserves delicate algorithm optimization due to the serious application scenario and strict requirement for the answer quality. In this paper, we introduce a novel Knowledge Abstraction Matching (KAM) method for the medical QA problem. The intuition of KAM is that there are many frequent repeat text segments appearing in the answers across different questions. From this view, we propose a new method that consists of frequent segment  N gram mining, medical knowledge abstraction, medical segment matching and answer reretrieval. KAM has been incorporated into Baiduu0027s enterprise medical QA system MelodyQA deployed on the backend of Muzhi Doctor. The evaluation shows that the proposed method can generate more quality answers for MelodyQA with a significant improvement of question coverage under acceptable accuracy.