Publications

From Hotel Reviews to City Similarities: A Unified Latent-Space Model

Published in Electronics, 9(1), 2020

In the context of hospitality management, a challenging research problem is to identify effective strategies to explain hotel reviews and ratings and their correlation with the urban context. Under this umbrella, the paper investigates the use of sentence-based embedding models to deeply explore the similarities and dissimilarities between cities in terms of the corresponding hotel reviews and the surrounding points of interests.

Recommended citation: Cagliero, L.; La Quatra, M.; Apiletti, D. From Hotel Reviews to City Similarities: A Unified Latent-Space Model. Electronics 2020, 9, 197. https://www.mdpi.com/2079-9292/9/1/197

Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview

Published in Trends and Applications of Text Summarization Techniques, 2019

The recent advances in multimedia and web-based applications have eased the accessibility to large collections of textual documents. To automate the process of document analysis, the research community has put relevant efforts into extracting short summaries of the document content.

Recommended citation: Cagliero, Luca, Paolo Garza, and Moreno La Quatra. "Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview." Trends and Applications of Text Summarization Techniques. IGI Global, 2020. 1-31. https://www.igi-global.com/chapter/combining-machine-learning-and-natural-language-processing-for-language-specific-multi-lingual-and-cross-lingual-text-summarization/235739

Poli2Sum@ CL-SciSumm-19: Identify, Classify, and Summarize Cited Text Spans by means of Ensembles of Supervised Models

Published in In 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) @ SIGIR 2019 (Vol. 2414, pp. 233–246), 2019

This paper presents the Poli2Sum approach to the 5th Computational Linguistics Scientific Document Summarization Shared Task (BIRNDL CL-SciSumm 2019).

Recommended citation: La Quatra, M., Cagliero, L., & Baralis, E. (2019). Poli2Sum@CL-SciSumm-19: Identify, Classify, and Summarize Cited Text Spans by means of Ensembles of Supervised Models. In 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) @ SIGIR 2019 (Vol. 2414, pp. 233–246). http://ceur-ws.org/Vol-2414/paper24.pdf