10 October 2016 |
Katarzyna Pakulska, Barbara Rychalska, Krystyna Chodorowska, Wojciech Walczak, Piotr Andruszkiewicz (Samsung) |
This seminar describes the winning solution designed for a core track within the SemEval 2016 English Semantic Textual Similarity task. The goal of the competition was to measure semantic similarity between two given sentences on a scale from 0 to 5. At the same time the solution should replicate human language understanding. The presented model is a novel hybrid of recursive auto-encoders from deep learning (RAE) and a WordNet award-penalty system, enriched with a number of other similarity models and features used as input for Linear Support Vector Regression. |
24 October 2016 |
Adam Przepiórkowski, Jakub Kozakoszczak, Jan Winkowski, Daniel Ziembicki, Tadeusz Teleżyński (Institute of Computer Science, Polish Academy of Sciences / University of Warsaw) |
The authors present resources created within CLARIN project aiming to help with qualitative evaluation of RTE systems: two textual derivations corpora and a corpus of textual entailment rules. Textual derivation is a series of atomic steps which connects Text with Hypothesis in a textual entailment pair. Original pairs are taken from the FraCaS corpus and a polish translation of the RTE3 corpus. Textual entailment rule sanctions textual entailment relation between the input and the output of a step, using syntactic patterns written in the UD standard and some other semantic, logical and contextual constraints expressed in FOL. |
7 November 2016 |
Rafał Jaworski (Adam Mickiewicz University in Poznań) |
The talk covers the Concordia algorithm which is used to maximize the productivity of a human translator. The algorithm combines the features of standard fuzzy translation memory searching with a concordancer. As the key non-functional requirement of computer-aided translation mechanisms is performance, Concordia incorporates upgraded versions of standard approximate searching techniques, aiming at reducing the computational complexity. |
21 November 2016 |
Norbert Ryciak, Aleksander Wawer (Institute of Computer Science, Polish Academy of Sciences) |
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The seminar presents initial experiments on recursive phrase-level sentiment computation using dependency syntax and deep learning. We discuss neural network architectures and implementations created within Clarin 2 and present results on English language resources. Seminar also covers undergoing work on Polish language resources. |
5 December 2017 |
Dominika Rogozińska, Marcin Woliński (Institute of Computer Science, Polish Academy of Sciences) |
The presentation shows methods of syntax disambiguation for Polish utterances produced by the Świgra parser. Presented methods include probabilistic context free grammars and maximum entropy models. The best of described models achieves efficiency measure at the level of 96.2%. The outcome of our experiments is a module for post-processing Świgra's parses. |
9 January 2017 |
Agnieszka Pluwak (Institute of Slavic Studies, Polish Academy of Sciences) |
The FrameNet project is defined by its authors as a lexical base with some ontological features (not an ontology sensu stricto, however, due to a selective approach towards description of frames and lexical units, as well as frame-to-frame relations). Ontologies, as knowledge representations in the field of NLP, should have the capacity of implementation to specific domains and texts, however, in the FrameNet bibliography published before January 2016 I haven’t found a single knowledge representation based entirely on frames or on an extensive structure of frame-to-frame relations. I did find a few examples of domain-specific knowledge representations with the use of selected FrameNet frames, such as BioFrameNet or Legal FrameNet, where frames were applied to connect data from different sources. Therefore, in my dissertation, I decided to conduct an experiment and build a knowledge representation of frame-to-frame relations for the domain of lease agreements. The aim of my study was the description of frames useful in case of building a possible data extraction system from lease agreements, this is frames containing answers to questions asked by a professional analyst while reading lease agreements. In my work I have asked several questions, e.g. would I be able to use FrameNet frames for this purpose or would I have to build my own frames? Will the analysis of Polish cause language-specific problems? How will the professional language affect the use of frames in context? Etc. |
23 January 2017 |
Marek Rogalski (Lodz University of Technology) |
Automatic paraphrasing |
Paraphrasing is conveying the essential meaning of a message using different words. The ability to paraphrase is a measure of understanding. A teacher asking student a question "could you please tell us using your own words ...", tests whether the student has understood the topic. On this presentation we will discuss the task of automatic paraphrasing. We will differentiate between syntax-level paraphrases and essential-meaning-level paraphrases. We will bring up several techniques from seemingly unrelated fields that can be applied in automatic paraphrasing. We will also show results that we've been able to produce with those techniques. |
6 February 2017 |
Łukasz Kobyliński (Institute of Computer Science, Polish Academy of Sciences) |
Korpusomat is a web tool facilitating unassisted creation of corpora for linguistic studies. After sending a set of text files they are automatically morphologically analysed and lemmatised using Morfeusz and disambiguated using Concraft tagger. The resulting corpus can be then downloaded and analysed offline using Poliqarp search engine to query for information related to text segmentation, base forms, inflectional interpretations and (dis)ambiguities. Poliqarp is also capable of calculating frequencies and applying basic statistical measures necessary for quantitative analysis. Apart from plain text files Korpusomat can also process more complex textual formats such as popular EPUBs, download source data from the Internet, strip unnecessary information and extract document metadata. |
20 February 2017 (invited talk at the Institute seminar) |
Elżbieta Hajnicz (Institute of Computer Science, Polish Academy of Sciences) |
The Polish Valence Dictionary (Walenty) is intended to be used by natural language processing tools, particularly parsers, and thus it offers formalized representation of the valency information. The talk presented the notion of valency and its representation in the dictionary along with examples illustrating how particular syntactic and semantic language phenomena are modelled. |
2 March 2017 |
Wojciech Jaworski (University of Warsaw) |
As part of the talk I will describe the division of texts into sentences and controlling the execution of each parser within the emerging hybrid parser in the Clarin-bis project. I will describe the adopted method of dependency structure conversion aimed to make them compatible with the structures of categorial parser. The conversion will have two aspects: changing the attributes of each node and changing the links between nodes. I will depict how the method used can be extended to convert compressed forests generated by the parser Świgra. At the end I wil talk about the plans and the goals of reimplementation of the MateParser algorithm. |
13 March 2017 |
Marek Kozłowski, Szymon Roziewski (National Information Processing Institute) |
The presentation shows how BabelNet (the multilingual encyclopaedia and semantic network based on publicly available data sources such as Wikipedia and WordNet), can be used in the task of grouping short texts, sentiment analysis or emotional profiling of movies based on their subtitles. The second part presents the work based on CommonCrawl – publicly available petabyte-size open repository of multilingual Web pages. CommonCrawl was used to build two models of Polish: n-gram-based and semantic distribution-based. |
20 March 2017 |
Jakub Szymanik (University of Amsterdam) |
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In this joint paper with Camilo Thorne, we study whether semantic complexity influences the distribution of generalized quantifiers in a large English corpus derived from Wikipedia. We consider the minimal computational device recognizing a generalized quantifier as the core measure of its semantic complexity. We regard quantifiers that belong to three increasingly more complex classes: Aristotelian (recognizable by 2-state acyclic finite automata), counting (k+2-state finite automata), and proportional quantifiers (pushdown automata). Using regression analysis we show that semantic complexity is a statistically significant factor explaining 27.29% of frequency variation. We compare this impact to that of other known sources of complexity, both semantic (quantifier monotonicity and the comparative/superlative distinction) and superficial (e.g., the length of quantifier surface forms). In general, we observe that the more complex a quantifier, the less frequent it is. |
27 March 2017 (invited talk at the institute seminar) |
Paweł Morawiecki (Institute of Computer Science, Polish Academy of Sciences) |
In the last few years, Deep Neural Networks (DNN) has become a tool that provides the best solution for many problems from image and speech recognition. Also in natural language processing DNN totally revolutionizes the way how translation or word representation is done (and for many other problems). This presentation aims to provide good intuitions related to the DNN, their core architectures and how they operate. I will discuss and suggest the tools and source materials that can help in the further exploration of the topic and independent experiments. |
3 April 2017 |
Katarzyna Budzynska, Chris Reed (Institute of Philosophy and Sociology, Polish Academy of Sciences / University of Dundee) |
Argument Corpora, Argument Mining and Argument Analytics (part I) |
Argumentation, the most prominent way people communicate, has been attracting a lot of attention since the very beginning of the scientific reflection. The Centre for Argument Technology has been developing the infrastructure for studying argument structures for almost two decades. Our approach demonstrate several characteristics. First, we build upon the graph-based standard for argument representation, Argument Interchange Format AIF (Rahwan et al., 2007); and Inference Anchoring Theory IAT (Budzynska and Reed, 2011) which allows us to capture dialogic context of argumentation. Second, we focus on a variety of aspects of argument structures such as argumentation schemes (Lawrence and Reed, 2016); illocutionary intentions speakers associate with arguments (Budzynska et al., 2014a); ethos of arguments' authors (Duthie et al., 2016); rephrase relation which paraphrases parts of argument structures (Konat et al., 2016); and protocols of argumentative dialogue games (Yaskorska and Budzynska, forthcoming). |
10 April 2017 |
Paweł Morawiecki (Institute of Computer Science, Polish Academy of Sciences) |
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For the last few years more and more problems in NLP have been successfully tackled with neural nets, particularly with deep architectures. These are such problems as sentiment analysis, topic classification, coreference, word representations and image labelling. In this talk i will give some details on most promising architectures used in NLP including recurrent and convolutional nets. The presented solutions will be given in a context of a concrete problem, namely the coreference problem in Polish language. |
15 May 2017 |
Katarzyna Budzynska, Chris Reed (Institute of Philosophy and Sociology, Polish Academy of Sciences / University of Dundee) |
Argument Corpora, Argument Mining and Argument Analytics (part II) |
In the second part of our presentation we will describe characteristics of argument structures using examples from our AIF corpora of annotated argument structures in various domains and genres (see also OVA+ annotation tool) including moral radio debates (Budzynska et al., 2014b); Hansard records of the UK parliamentary debates (Duthie et al., 2016); e-participation (Konat et al., 2016; Lawrence et al., forthcoming); and the US 2016 presidential debates (Visser et al., forthcoming). Finally, we will show how such complex argument structures, which on the one hand make the annotation process more time-consuming and less reliable, can on the other hand result in automatic extraction of a variety of valuable information when applying technologies for argument mining (Budzynska and Villata, 2017; Lawrence and Reed, forthcoming) and argument analytics (Reed et al., forthcoming). |
12 June 2017 (invited talk at the Institute seminar) |
Adam Pawłowski (University of Wroclaw) |
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The subject of my lecture is the phenomenon of sequentiality in linguistics. Sequentiality is defined here as a characteristic feature of a text or of a collection of texts, which expresses the sequential relationship between units of the same type, ordered along the axis of time or according to a different variable (e.g. the sequence of reading or publishing). In order to model sequentiality which is thus understood, we can use, among others, time series, spectral analysis, theory of stochastic processes, theory of information or some tools of acoustics.Referring to both my own research and existing literature, in my lecture I will be presenting sequential structures and selected models thereof in continuous texts, as well as models used in relation to sequences of several texts (known as chronologies of works); I will equally mention glottochronology, which is a branch of quantitative linguistics that aims at mathematical modeling of the development of language over long periods of time. Finally, I will relate to philosophical attempts to elucidate sequentiality (the notion of the text’s ‘memory’, the result chain, Pitagoreism, Platonism). |