Language processing architectures are often evaluated in near-to-perfect conditions with respect to processed content. The tools which perform sufficiently well on electronic press, books and other type of non-interactive content may poorly handle littered, colloquial and multilingual textual data which make the majority of communication today. This paper aims at investigating how Polish Twitter data (in a slightly controlled ‘political’ flavour) differs from expectation of linguistic tools and how they could be corrected to be ready for processing by standard language processing chains available for Polish. The setting includes specialised components for spelling correction of tweets as well as hashtag and username decoding.

This paper attempts a preliminary interpretation of the occurrence of different types of linguistic constructs in the manually-annotated Polish Coreference Corpus by providing analyses of various statistical properties related to mentions, clusters and near-identity links. Among others, frequency of mentions, zero subjects and singleton clusters is presented, as well as the average mention and cluster size. We also show that some coreference clustering constraints, such as gender or number agreement, are frequently not valid in case of Polish. The need for lemmatization for automatic coreference resolution is supported by an empirical study. Correlation between cluster and mention count within a text is investigated, with short characteristics of outlier cases. We also examine this correlation in each of the 14 text domains present in the corpus and show that none of them has abnormal frequency of outlier texts regarding the cluster/mention ratio. Finally, we report on our negative experiences concerning the annotation of the near-identity relation. In the conclusion we put forward some guidelines for the future research in the area.

This article presents the Polish Summaries Corpus, a new resource created to support the development and evaluation of the tools for automated single-document summarization of Polish. The Corpus contains a large number of manual summaries of news articles, with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard. There are several summarizers developed specifically for Polish language, but their in-depth evaluation and comparison was impossible without a large, manually created corpus. We present in detail the process of text selection, annotation process and the contents of the corpus, which includes both abstract free-word summaries, as well as extraction-based summaries created by selecting text spans from the original document. Finally, we describe how that resource could be used not only for the evaluation of the existing summarization tools, but also for studies on the human summarization process in Polish language.

Although the availability of the natural language processing tools and the development of metrics to evaluate them increases, there is a certain gap to fill in that field for the less-resourced languages, such as Polish. Therefore the projects which are designed to extend the existing tools for diverse languages are the best starting point for making these languages more and more covered. This paper presents the results of the first attempt of the co\-re\-fe\-rence resolution for Polish using statistical methods. It presents the conclusions from the process of adapting the Beautiful Anaphora Resolution Toolkit (BART; a system primarily designed for the English language) for Polish and collates its evaluation results with those of the previously implemented rule-based system. Finally, we describe our plans for the future usage of the tool and highlight the upcoming research to be conducted, such as the experiments of a larger scale and the comparison with other machine learning tools.