@inproceedings{a936e7b217bc43f09388588133b2ffa4,
title = "MapIntel: Enhancing Competitive Intelligence Acquisition Through Embeddings and Visual Analytics",
abstract = "Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing module also leverages the embeddings by projecting them onto two dimensions while preserving the original landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. In this work, we evaluate the system and its components on the 20 newsgroups dataset and demonstrate the superiority of Transformer-based components.",
keywords = "Competitive Intelligence, Information retrieval, Sentence embeddings, Topic modeling, Transformer architecture, Visual analytics",
author = "David Silva and Fernando Bacao",
note = "info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0116%2F2019/PT# Silva, D., & Bacao, F. (2022). MapIntel: Enhancing Competitive Intelligence Acquisition Through Embeddings and Visual Analytics. In G. Marreiros, B. Martins, A. Paiva, A. Sardinha, & B. Ribeiro (Eds.), Progress in Artificial Intelligence: 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31–September 2, 2022, Proceedings (pp. 599-610). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13566 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16474-3_49 -- Funding Information: This work was supported by the Funda{\c c}{\~a}o para a Ci{\^e}ncia e Tecnologia of Minist{\'e}rio da Ci{\^e}ncia e Tecnologia e Ensino Superior (research grant under the DSAIPA/DS/0116/2019 project). ; 21st EPIA Conference on Artificial Intelligence, EPIA 2022 ; Conference date: 31-08-2022 Through 02-09-2022",
year = "2022",
month = sep,
day = "13",
doi = "10.1007/978-3-031-16474-3_49",
language = "English",
isbn = "978-3-031-16473-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "599--610",
editor = "Goreti Marreiros and Bruno Martins and Ana Paiva and Alberto Sardinha and Bernardete Ribeiro",
booktitle = "Progress in Artificial Intelligence",
address = "Germany",
}