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 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 28th 2015, 13h00 - 14h00

Wang Ling

N L P w i t h c h a r a c t e r s

Anfiteatro do Complexo Interdisciplinar

Instituto Superior Técnico - Alameda


We present a neural network model that computes embeddings of words using recurrent network based on long short-term memories to read in characters. As an alternative to word lookup tables that require a set of parameters for every word type in the vocabulary, our models only require a look up table for characters and a fixed number of parameters for the compositional model, independent of the vocabulary size. As a consequence, our model uses fewer parameters and is also sensitive to lexical content, such as morphology, making it more adequate for tasks where morphological information is required. In part-of-speech tagging, we can perform competitively with state-of-the-art systems, without explicitly engineering lexical features, and using a relatively small number of parameters.


Bio: Wang Ling is a student of the dual Ph.D. program in Computer Science between Carnegie Mellon University and Instituto Superior Técnico, where he also received his master degree in 2009. His Ph.D. work focuses on Machine Translation in noisy domains, such as Twitter, and Deep Learning for NLP.