This paper introduces AnnoHID, a semi-automated annotation framework designed for medical texts in low-resource languages. The system integrates large language models (LLMs) for pre-annotation and human validation to support efficient and consistent annotation. We demonstrate its application to Bahasa Indonesia medical social media texts from Alodokter, a medical Q A platform, for Named Entity Recognition (NER) and Medical Concept Normalization (MCN). We conducted a user study with 21 participants to demonstrate the effectiveness of AnnoHID. The results show that LLM-assisted annotation yields higher inter-annotator agreement for both NER (𝜅 = 0.76) and MCN (𝜅 = 0.63) and that human review improves raw LLM NER output, raising the F1 score from 0.39 to 0.45. However, LLM assistance did not reduce annotation time and may introduce normalization bias in MCN. The framework is multilingual, human-in-the-loop, and interoperable with standard medical terminologies, such as SNOMED-CT. Future work focuses on mitigating pre-annotation bias, reducing annotation overhead, and scaling evaluations to larger datasets and additional low-resource languages.

Approaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals’ writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used.

This paper describes the systems submitted by GadjahMada team to the Native Language Identification (NLI) Shared Task 2017. Our models used a continuous representation of character n-grams which are learned jointly with feed-forward neural network classifier. Character n-grams have been proved to be effective for style-based identification tasks including NLI. Results on the test set demonstrate that the proposed model performs very well on essay and fusion tracks by obtaining more than 0.8 on both F-macro score and accuracy.

This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.