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. |
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11 October 2021 (NOTE: the seminar will start at 10:00) |
Adam Przepiórkowski (Institute of Computer Science, Polish Academy of Sciences / University of Warsaw) |
Jakie kwantyfikatory można wyrazić w języku naturalnym? O koordynacji i kwantyfikatorach poliadycznych |
Streszczenie wystąpienia podamy już niedługo. |
18 October 2021 |
Jan Kocoń, Przemysław Kazienko (Wrocław University of Technology) |
Personalized NLP |
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. |
8 November 2021 |
Ryszard Tuora, Łukasz Kobyliński (Institute of Computer Science, Polish Academy of Sciences) |
Dependency Trees in Automatic Inflection of Multi Word Expressions in Polish |
Talk summary will be available shortly. |
22 November 2021 (NOTE: the seminar will start at 10:00) |
Joanna Byszuk (Institute of Polish Language, Polish Academy of Sciences) |
Talk title will be available shortly |
Talk summary will be available shortly. |
29 November 2021 |
Piotr Przybyła (Institute of Computer Science, Polish Academy of Sciences) |
When classification accuracy is not enough: Explaining news credibility assessment and measuring users' reaction |
Talk summary will be available shortly. |
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.