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

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 (NOTE: The seminar will start at 12:30!)

Agnieszka Patejuk (Institute of Computer Science, Polish Academy of Sciences / University of Oxford), Adam Przepiórkowski (Institute of Computer Science, Polish Academy of Sciences / University of Warsaw)

Coordination in the Universal Dependencies standard  

Universal Dependencies (UD; https://universaldependencies.org/) is a widespread syntactic annotation scheme employed by many parsers of multiple languages. However, the scheme does not adequately represent coordination, i.e., structures involving conjunctions. In this talk, we propose representations of two aspects of coordination which have not so far been properly represented either in UD or in dependency grammars: coordination of unlike grammatical functions and nested coordination.

4 November 2019

Marcin Będkowski (Educational Research Institute), Łukasz Kobyliński (Institute of Computer Science, Polish Academy of Sciences)

The title of the talk will be available shortly  

The summary of the talk will be available shortly.

18 November 2019

Alexander Rosen (Charles University in Prague)

The InterCorp multilingual parallel corpus: representation of grammatical categories  

InterCorp, a multilingual parallel component of the Czech National Corpus, has been on-line since 2008, growing steadily to its present size of 1.7 billion words in 40 languages. A substantial share of fiction is complemented by legal and journalistic texts, parliament proceedings, film subtitles and the Bible. The texts are sentence-aligned and – in most languages – tagged and lemmatized. We will focus on the issue of morphosyntactic annotation, currently using language-specific tagsets and tokenization rules, and explore various solutions, including those based on the guidelines, data and tools developed in the Universal Dependencies project.

21 November 2019

Alexander Rosen (Charles University in Prague)

A learner corpus of Czech  

Texts produced by language learners (native or non-native) include all sorts of non-canonical phenomena, complicating the task of linguistic annotation while requiring an explicit markup of deviations from the standard. Although a number of English learner corpora exist and other languages have been catching up recently, a commonly accepted approach to designing an error taxonomy and annotation scheme has not emerged yet. For !CzeSL, the corpus of Czech as a Second Language, several such approaches were designed and tested, later extended also to texts produced by Czech schoolchildren. I will show various pros and cons of these approaches, especially with a view of Czech as a highly inflectional language with free word order.

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