The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of his/her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the mailing list webpage. Anyone can attend the seminars; no registration is necessary. If you would like to present something, please send us an email.

The seminars are usually held every other Tuesday, from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Tuesday, April 4th 2017, 13h00 - 14h00

Filipa Peleja (Vodafone)

Linguistic Benchmarks of Online News Article Quality

Anfiteatro PA2

Instituto Superior Técnico - Alameda


Online news editors ask themselves the same question many times: what is missing in this news article to go online? This is not an easy question to be answered by computational linguistic methods. In this work, we address this important question and characterise the constituents of news article editorial quality. More specifically, we identify 14 aspects related to the content of news articles. Through a correlation analysis, we quantify their independence and relation to assessing an article’s editorial quality. We also demonstrate that the identified aspects, when combined together, can be used effectively in quality control methods for online news.


Bio: Filipa Peleja is a data scientist in the Big Data Analytics team at Vodafone. She holds a Ph.D. in Computer Science having studied topics in machine learning, information retrieval, natural language processing, sentiment analysis and recommendation systems. During her Ph.D. she had the opportunity to enroll in a nine month internship at Yahoo! Labs and work as a data scientist researcher at Eurecat Technology Centre of Catalonia. Filipa Peleja has been involved in several computer software projects in collaboration with private companies, public institutions and academia. She studied at the Faculty of Science and Technology in the NOVA University of Lisbon obtaining Computer Software/Informatics Engineering (M.Sc., B.Sc.). Also, worked as a Business Consultant at IP2CS and as a researcher at research centers CITI and NOVALINCS. Filipa Peleja has published several scientific articles and demos in international conferences.

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