A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis
Antonio Origlia, Martina Di Bratto, Maria Di Maro, Sabrina Mennella
Abstract
In dialogue analysis, characterising named entities in the domain of interest is relevant in order to understand how people are making use of them for argumentation purposes. The movie recommendation domain is a frequently considered case study for many applications and by linguistic studies and, since many different resources have been collected throughout the years to describe it, a single database combining all these data sources is a valuable asset for cross-disciplinary investigations. We propose an integrated graph-based structure of multiple resources, enriched with the results of the application of graph analytics approaches to provide an encompassing view of the domain and of the way people talk about it during the recommendation task. While we cannot distribute the final resource because of licensing issues, we share the code to assemble and process it once the reference data have been obtained from the original sources.- Anthology ID:
- 2022.lrec-1.138
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1297–1306
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.138
- DOI:
- Cite (ACL):
- Antonio Origlia, Martina Di Bratto, Maria Di Maro, and Sabrina Mennella. 2022. A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1297–1306, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis (Origlia et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.138.pdf
- Data
- Inspired, MovieLens