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, normally at 10:15 am, in the seminar room of the ICS PAS (ul. Jana Kazimierza 5, Warszawa). All recorded talks are available on YouTube.

23 September 2019

Igor Boguslavsky (Institute for Information Transmission Problems, Russian Academy of Sciences / Universidad Politécnica de Madrid)

Semantic analysis based on inference  

I will present a semantic analyzer SemETAP, which is a module of a linguistic processor ETAP designed to perform analysis and generation of NL texts. We proceed from the assumption that the depth of understanding is determined by the number and quality of inferences we can draw from the text. Extensive use of background knowledge and inferences permits to extract implicit information.

Salient features of SemETAP include:

— knowledge base contains both linguistic and background knowledge;

— inference types include strict entailments and plausible expectations;

— words and concepts of the ontology may be supplied with explicit decompositions for inference purposes;

— two levels of semantic structure are distinguished. Basic semantic structure (BSemS) interprets the text in terms of ontological elements. Enhanced semantic structure (EnSemS) extends BSemS by means of a series of inferences;

— a new logical formalism Etalog is developed in which all inference rules are written.

7 October 2019 (NOTE: the seminar will start at 13:00!)

Tomasz Stanisz (Institute of Nuclear Physics, Polish Academy of Sciences)

What can a complex network say about a text?  

Complex networks, which have found application in the quantitative description of many different phenomena, have proven to be useful in research on natural language. The network formalism allows to study language from various points of view - a complex network may represent, for example, distances between given words in a text, semantic similarities, or grammatical relationships. One of the types of linguistic networks are word-adjacency networks, which describe mutual co-occurrences of words in texts. Although simple in construction, word-adjacency networks have a number of properties allowing for their practical use. The structure of such networks, expressed by appropriately defined quantities, reflects selected characteristics of language; applying machine learning methods to collections of those quantities may be used, for example, for authorship attribution.

21 October 2019

Piotr Przybyła, Jakub Gąsior (Institute of Computer Science, Polish Academy of Sciences)

Two tasks of credibility assessment based on short text fragments: detecting bots on social media and extracting verifiable statements in political discussions  

The summary of the talk will be available shortly.

18 November 2019

Alexander Rosen (Charles University in Prague)

The title of the talk will be available shortly  

The summary of the talk will be available shortly.

21 November 2019

Alexander Rosen (Charles University in Prague)

The title of the talk will be available shortly  

The summary of the talk will be available shortly.

Please see also the talks given in 2000–2015 and 2015–2019.