• Automatic question answering for medical MCQs: Can it go further than information retrieval?

      Ha, Le An; Yaneva, Viktoriya (RANLP, 2019-09-04)
      We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require training data. We evaluate the system performance on a challenging set of university-level medical science multiple-choice questions. Best performance is achieved when combining a neural approach with an IR approach, both of which work independently. Unlike previous approaches, the system achieves statistically significant improvement over the random guess baseline even for questions that are labeled as challenging based on the performance of baseline solvers.
    • Natural language processing for mental disorders: an overview

      Calixto, Iacer; Yaneva, Viktoriya; Cardoso, Raphael (CRC Press, 2022-12-31)
    • A survey of the perceived text adaptation needs of adults with autism

      Yaneva, Viktoriya; Orasan, Constantin; Ha, L; Ponomareva, Natalia (RANLP, 2019-09-02)
      NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups. One such group are adults with high-functioning autism, who are usually able to read long sentences and comprehend difficult words but whose comprehension may be impeded by other linguistic constructions. This is especially challenging for real-world usergenerated texts such as product reviews, which cannot be controlled editorially and are thus in a stronger need of automatic adaptation. To address this problem, we present a mixedmethods survey conducted with 24 adult webusers diagnosed with autism and an agematched control group of 33 neurotypical participants. The aim of the survey is to identify whether the group with autism experiences any barriers when reading online reviews, what these potential barriers are, and what NLP methods would be best suited to improve the accessibility of online reviews for people with autism. The group with autism consistently reported significantly greater difficulties with understanding online product reviews compared to the control group and identified issues related to text length, poor topic organisation, identifying the intention of the author, trustworthiness, and the use of irony, sarcasm and exaggeration.