Analyzing Geographic Questions Using Embedding-based Topic Modeling
Bibliographic Data
| ID | 22033716 |
|---|---|
| Authors | JongHyeon Yang (0000-0003-4827-242X, Seoul National University), Hanme Jang (0000-0003-3895-4224, Seoul National University), Kiyun Yu (Seoul National University, corresponding author) |
| Year | 2023 |
| Volume | 12 |
| Issue | 2 |
| Pages | 52 |
| Publication date | 2023-02-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12020052 |
| OpenAlex | W4319298083 |
| Language | EN |
| Citations received | 1 |
| References cited | 21 |
Recently, open-domain question-answering systems have achieved tremendous progress because of developments in large language models (LLMs), and have successfully been applied to question-answering (QA) systems, or Chatbots. However, there has been little progress in open-domain question answering in the geographic domain. Existing open-domain question-answering research in the geographic domain relies heavily on rule-based semantic parsing approaches using few data. To develop intelligent GeoQA agents, it is crucial to build QA systems upon datasets that reflect the real users’ needs regarding the geographic domain. Existing studies have analyzed geographic questions using the geographic question corpora Microsoft MAchine Reading Comprehension (MS MARCO), comprising real-world user queries from Bing in terms of structural similarity, which does not discover the users’ interests. Therefore, we aimed to analyze location-related questions in MS MARCO based on semantic similarity, group similar questions into a cluster, and utilize the results to discover the users’ interests in the geographic domain. Using a sentence-embedding-based topic modeling approach to cluster semantically similar questions, we successfully obtained topic models that could gather semantically similar documents into a single cluster. Furthermore, we successfully discovered latent topics within a large collection of questions to guide practical GeoQA systems on relevant questions
Data science · Information retrieval · Latent semantic analysis · Natural language processing · Open domain · Parsing · Question answering · Semantic similarity · Sentence · Topic model · World Wide Web · Computer Science · Data Quality and Management · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |