Community Question Answering : an Investigation into the Influence of Question Expansion based on User's Explicit Information on Learning to Rank Q&A Models

Question Answering (Q&A) community forums provide an open and collaborative environment where users can post subjective questions and answers based on their life experiences or knowledge of specific domains. User interaction in online forums generates thousands of comments on different subjects, resulting in a massive amount of rich text every year. Posts in Q\&A online communities contain rich information that helps NLP scientists and developers propose approaches to help questioners find answers to their questions based on previously submitted similar questions. However, community questions are short and require additional textual information for automatic matching with the relevant long answers. Explicit information about the question is crucial for accurately predicting the ranked list of answer candidates. Community answer retrieval aims to answer new user questions by leveraging answers from previous users' questions. Automated Learning-to-Rank (LTR) models for Community Question Answering (CQA) require additional explicit information to appropriately expand questions, enabling the identification of an accurate ranked list of answer candidates based on their relevance to the question. This doctoral thesis proposes an investigation into the importance of different explicit information for expanding user questions in transformer-based ranking models. User tags and question descriptions are the explicit information I observed in this study to expand user question information. A key contribution of this study is a novel automated tag classification approach for identifying domain-specific tags in question descriptions, which enables a comparison of ranking model performance by incorporating both user- and automatically selected tags as inputs. The proposed automatic tag classification helps users find adequate tags to summarise a long question description into a tag set. This study also presents new annotated datasets containing thousands of user questions in two domains (personal finances and home improvements). This thesis concludes that incorporating explicit user information for question expansion enhances the predictive performance of LTR models in sorting the answer candidate list by relevance. Among the models employed in this study, those using question descriptions as additional information to expand user questions outperform other input configurations, achieving precision rates exceeding 90\% for rank-aware measures such as Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP). The experiments further reveal that including automatically selected tags and user tags as part of the input yields comparable performance across all LTR CQA models. This study helps retrieval systems answer new questions based on corresponding comments used to answer old questions.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Universität Passau

Year

2026

Author dc:creator

• Sousa Maia, Macedo

Contributors dc:contributor

• Endres, Markus

• transformerbased ranking models
• user tags
• question description
• tag classification

Statement dc:rights

• Creative Commons - CC BY - Namensnennung 4.0 International

Repository record source_url

https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/2122

OAI identifier oai:identifier

oai:kobv.de-opus4-uni-passau:2122