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 group webpage. Anyone can attend the seminars. 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, June 15th 2021, 13h00 - 14h00

Sofia Teixeira (Hospital da Luz Learning Health)

Revealing semantic and emotional structure of suicide notes

Location (webinar): (Zoom)


Understanding how people who commit suicide perceive their cognitive states and emotions represents a crucially open scientific challenge. We build upon cognitive network science, psycholinguistics, and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the semantic ideas and emotional states of people who committed suicide through structural balance theory, semantic prominence, and emotional profiling. Our results show that suicide notes have a higher degree of balance than one would expect in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. This is reflected in a positive clustering where positively perceived concepts are prominently central and are found to cluster together, reducing contrast with more peripheral and negative concepts. Combining semantic frames with emotional data, we find that a key positive concept is “love” and that the emotions populating its semantic frame combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.


Bio: Sofia Teixeira holds a PhD in Information Systems and Computer Engineering from Universidade de Lisboa (Portugal). Currently, she works as a research scientist at Hospital da Luz Learning Health in Lisbon. Previously, she was a postdoc at the Indiana University Network Science Institute. Sofia's research interests include modelling and analyzing complex systems through the development of new algorithms on graphs and the application of network science and machine learning methods.

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