Natural Language Processing Seminar 2024–2025
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, often online – please use the link next to the presentation title. All recorded talks are available on YouTube. |
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7 October 2024 |
Janusz S. Bień (University of Warsaw, profesor emeritus) |
Some glyphs from 16th century fonts, described in the monumental work “Polonia Typographica Saeculi Sedecimi”, can be more or less easily identified with the Unicode standard characters. Some glyphs don't have Unicode codepoints, but can be printed with an appropriate OpenType/TrueType fonts using typographic features. For some of them their encoding remains an open question. Some examples will be discussed. |
14 October 2024 |
Alexander Rosen (Charles University in Prague) |
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This study examines metrics of syntactic complexity (SC) and lexical diversity (LD) as tools for analyzing linguistic variation within and across languages. Using quantifiable measures based on cross-linguistically consistent (morpho)syntactic annotation (Universal Dependencies), the research utilizes parallel texts from a large multilingual corpus (InterCorp). Six SC and two LD metrics – covering the length and embedding levels of nominal and clausal constituents, mean dependency distance (MDD), and sentence length – are applied as metadata for sentences and texts. |
The presentation will address how these metrics can be visualized and incorporated into corpus queries, how they reflect structural differences across languages and text types, and whether SC and LD vary more across languages or text types. It will also consider the impact of language-specific annotation nuances and correlations among the measures. The analysis includes comparative examples from Polish, Czech, and other languages. |
Preliminary findings indicate higher SC in non-fiction compared to fiction across languages, with nominal and clausal metrics being dominant factors. The results suggest distinct patterns for MDD and sentence length, highlighting the impact of structural differences (e.g., analytic vs. synthetic morphology, dominant word-order patterns) and the influence of source text type and style. |
21 October 2024 |
Rafał Jaworski (Adam Mickiewicz University in Poznań) |
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The summary of the talk will be here shortly. |
4 November 2024 |
Jakub Kozakoszczak (Deutsche Telekom) |
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The summary of the talk will be made available shortly. |
21 November 2024 |
Christian Chiarcos (University of Augsburg) |
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The summary of the talk will be made available shortly. |
2 December 2024 |
Participants of PolEval 2024 |
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The program will be made available after the contest ends. |
19 December 2024 |
Piotr Przybyła (Pompeu Fabra University / Institute of Computer Science, Polish Academy of Sciences) |
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The summary of the talk will be made available shortly. |
Please see also the talks given in 2000–2015 and 2015–2023. |
11 March 2024
Mateusz Krubiński (Charles University in Prague)
Talk summary will be made available soon.
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.