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. |
|
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) |
|
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. |
28 October 2024 |
Rafał Jaworski (Adam Mickiewicz University in Poznań) |
The presentation will cover my research in the field of natural language processing for computer-aided translation. In particular, I will present the Inter-language Vector Space algorithm set for aligning sentences at the word and phrase level using multilingual word embeddings. |
The first function of the set is used to generate vector representations of words. They are generated using an auto-encoder neural network based on text data – a text corpus. In this way vector dictionaries for individual languages are created. The vector representations of words in these dictionaries constitute vector spaces that differ between languages. |
To solve this problem and obtain vector representations of words that are comparable between languages, the second function of the Inter-language Vector Space set is used. It is used to align vector spaces between languages using transformation matrices calculated using the singular value decomposition method. This matrix is calculated based on homonyms, i.e. words written identically in the language of space X and Y. Additionally, a bilingual dictionary is used to improve the results. The transformation matrix calculated in this way allows for adjusting space X in such a way that it overlaps space Y to the maximum possible extent. |
The last function of the set is responsible for creating a multilingual vector space. The vector space for the English language is first added to this space in its entirety and without modification. Then, for each other vector space, the transformation matrix of this space to the English space is first calculated. The vectors of the new space are multiplied by this matrix and thus become comparable to the vectors representing English words. |
The Inter-language Vector Space algorithm set is used in translation support systems, for example in the author's algorithm for automatic transfer of untranslated tags from the source sentence to the target one. |
4 November 2024 |
Jakub Kozakoszczak (Deutsche Telekom) |
|
Attempts at building regex patterns that match information annotated in the text with embedded markup lead to prohibitively unmanageable patterns. Regex and markup combine even worse when the pattern must use distances as a matching condition because tags disrupt the text format. On the other hand, fully externalized markup preserves text format but leaves regex patterns without reference points. |
I introduce the Zero Insertion Markup Language (ZIML), where every combination of characters and labels in the annotated text is represented by a unique "allocharacter". Regex patterns also translate to appropriate patterns with allocharacters, preserving text span matches in standard regex engines. As the main result, ZIML extends regex semantics to include label referencing by matching allocharacters that represent them. |
I will give a proof of correctness for ZIML translation and demonstrate its implementation, including a user-facing pattern language that integrates labels into regex syntax. I hope to discuss potential applications of ZIML in linguistics and natural language processing. A basic understanding of model theory and regex functionality is recommended. |
21 November 2024 |
Christian Chiarcos (University of Augsburg) |
|
Semantic technologies comprise a broad set of standards and technologies including aspects of knowledge representation, information management and computational inference. In this lecture, I will describe the application of knowledge representation standards to the realm of computational discourse, and especially, the annotation of discourse relations. In particular, this includes the formal modelling of discourse relations of different theoretical frameworks by means of modular, interlinked ontologies, the machine-readable edition of discourse marker inventories with OntoLex and techniques for the induction of discourse marker inventories. |
19 December 2024 |
Piotr Przybyła (Pompeu Fabra University / Institute of Computer Science, Polish Academy of Sciences) |
|
The presentation will cover XARELLO: a generator of adversarial examples for testing the robustness of text classifiers based on reinforcement learning. This solution is adaptive: it learns from previous successes and failures in order to better adjust to the vulnerabilities of the attacked model. It reflects the behaviour of a persistent and experienced attacker, which are common in the misinformation-spreading environment. We will cover the evaluation of the approach using several victim classifiers and credibility-assessment tasks, showing it generates better-quality examples with less queries, and is especially effective against the modern LLMs. |
17 February 2025 |
Ryszard Staruch, Filip Graliński (Adam Mickiewicz University in Poznań) |
|
Talk summary will be made available soon. |
23 March 2025 |
Maciej Rapacz |
|
Talk summary will be made available soon. |
Please see also the talks given in 2000–2015 and 2015–2024. |
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.