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The Artificial Intelligence Laboratory of Udine (AILAB Udine) was founded in 1984 by Professor Carlo Tasso with the aim of producing research in the areas of artificial intelligence, machine learning, knowledge-based systems, user modeling, personalization of Web sites and portals, intelligent interfaces for information retrieval and information filtering, enterprise information portals for knowledge management, e-learning and information sharing. The laboratory has a 30 years long history of innovation and active support to local activities.

Prospective PHD Students (Call is Open – Deadline July 20, 2018)

If you are a prospective student interested in Artificial Intelligence, Machine Learning and Deep Learning Research at the Universirty of Udine, please read about our Ph.D. admissions process and contact us (Carlo Tasso and Giuseppe Serra). If you are applying to our Ph.D. course in Computer Science and are interested in our research, please state this in your statement of purpose.

Arabic Keyphrase Extraction

Arabic keyphrase extraction is a crucial task due to the significant and growing amount of Arabic text on the web generated by a huge population. It is becoming a challenge for the community of Arabic natural language processing because of the severe shortage of resources and published processing systems. In this paper we propose a deep learning based approach for Arabic keyphrase extraction that achieves better performance compared to the related competitive approaches. We also introduce the community with an annotated large-scale dataset of about 6000 scientific abstracts which can be used for training, validating and evaluating deep learning approaches for Arabic keyphrase extraction. Related publications: Helmy M., Vigneshram R. M., Serra G., Tasso C. Applying Deep Learning for Arabic Keyphrase Extraction. In: Proc. of the 4th International Conference on Arabic Computational Linguistics (ACLing 2018), November 17-19 2018, Dubai, United Arab Emirates. Resources: Arabic Abstracts Dataset

Automatic Keyphrase Extraction

Keyphrases (KPs) are phrases that “capture the main topic discussed on a given document”. More specifically, KPs are phrases typically one to five words long that appear verbatim in a document, and can be used to briefly summarize its content. The task of finding such KPs is called Automatic Keyphrase Extraction (AKE). Recently, AKE has received a lot of attention, because it has been successfully used in many natural language processing (NLP) tasks, such as text summarization, document clustering, or non-NLP tasks such as social network analysis or user modeling. AKE  approaches have been also applied in Information Retrieval of relevant documents in digital document archives which can contain heterogeneous types of items, such as books articles, papers etc. However, given the wide variety of lexical, linguistic and semantic aspects that can contribute to define a keyphrase, it difficult to design hand-crafted feature, and even the best performing algorithms hardly reach F1-Scores of 50% on the most common evaluation sets. […]

Bidirectional LSTM Recurrent Neural Networkfor Keyphrase Extraction

Bidirectional LSTM Recurrent Neural Networkfor Keyphrase Extraction  Basaldella M, Antolli E, Serra G, Tasso C. Italian Research Conference on Digital Libraries (IRCDL), 2018

Predicting the Usefulness of Amazon Reviews Using Argumentation Mining

Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis. Related publications: Passon M., Lippi M., Serra G., Tasso C. Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining. In: Proc. of Workshop on Argument Mining, Brussels, Belgium, 2018.