Natural Language Processing Seminar 2021–2022
The NLP Seminar is organised by the Linguistic Engineering Group at the Institute of Computer Science, Polish Academy of Sciences (ICS PAS). It takes place on (some) Mondays, usually at 10:15 am, currently online – please use the link next to the presentation title. All recorded talks are available on YouTube. |
11 October 2021 |
Adam Przepiórkowski (Institute of Computer Science, Polish Academy of Sciences / University of Warsaw) |
The aim of this talk is to provide a semantic analysis of a construction – Heterofunctional Coordination – which is typical of Slavic and some neighbouring languages. In this construction, expressions bearing different grammatical functions may be conjoined. In this talk, I will propose a semantic analysis of such constructions based on the concept of generalized quantifiers (Mostowski; Lindström; Barwise and Cooper), and more specifically – polyadic quantifiers (van Benthem; Keenan; Westerståhl). Some familiarity with the language of predicate logic should suffice to fully understand the talk; all linguistic concepts (including "coordination", "grammatical functions") and logical concepts (including "generalized quantifiers" and "polyadic quantifiers") will be explained in the talk. |
18 October 2021 |
Przemysław Kazienko, Jan Kocoń (Wrocław University of Technology) |
Many natural language processing tasks, such as classifying offensive, toxic, or emotional texts, are inherently subjective in nature. This is a major challenge, especially with regard to the annotation process. Humans tend to perceive textual content in their own individual way. Most current annotation procedures aim to achieve a high level of agreement in order to generate a high quality reference source. Existing machine learning methods commonly rely on agreed output values that are the same for all annotators. However, annotation guidelines for subjective content can limit annotators' decision-making freedom. Motivated by moderate annotation agreement on offensive and emotional content datasets, we hypothesize that a personalized approach should be introduced for such subjective tasks. We propose new deep learning architectures that take into account not only the content but also the characteristics of the individual. We propose different approaches for learning the representation and processing of data about text readers. Experiments were conducted on four datasets: Wikipedia discussion texts labeled with attack, aggression, and toxicity, and opinions annotated with ten numerical emotional categories. All of our models based on human biases and their representations significantly improve prediction quality in subjective tasks evaluated from an individual's perspective. Additionally, we have developed requirements for annotation, personalization, and content processing procedures to make our solutions human-centric. |
Please see also the talks given in 2000–2015 and 2015–2020. |
2 April 2020
Stan Matwin (Dalhousie University)
Efficient training of word embeddings with a focus on negative examples

This presentation is based on our AAAI 2018 and AAAI 2019 papers on English word embeddings. In particular, we examine the notion of “negative examples”, the unobserved or insignificant word-context co-occurrences, in spectral methods. we provide a new formulation for the word embedding problem by proposing a new intuitive objective function that perfectly justifies the use of negative examples. With the goal of efficient learning of embeddings, we propose a kernel similarity measure for the latent space that can effectively calculate the similarities in high dimensions. Moreover, we propose an approximate alternative to our algorithm using a modified Vantage Point tree and reduce the computational complexity of the algorithm with respect to the number of words in the vocabulary. We have trained various word embedding algorithms on articles of Wikipedia with 2.3 billion tokens and show that our method outperforms the state-of-the-art in most word similarity tasks by a good margin. We will round up our discussion with some general thought s about the use of embeddings in modern NLP.