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Banko and Brill (2001) suggested that the develop- ment of very large training corpora may be more ef- fective for progress in empirical Natural Language Processing than improving methods that …...

Statistical machine translation relies heavily on the available training data. In some cases it is necessary to limit the amount of training data that can be created for or actually …...

AbstractIn Chinese texts, words composed of single or multiple characters are not separated by spaces, unlike most western languages. Therefore Chinese word segmentation is considered an important first step in …...

Several researchers have proposed semi-supervised learning methods for adapting event extraction systems to new event types. This paper investigates two kinds of bootstrapping methods used for event extraction: the document-centric …...

We report on efforts to build large-scale translation systems for eight European language pairs. We achieve most gains from the use of larger training corpora and basic modeling, but also …...

In this paper we address the problem of modeling compositional meaning for phrases and sentences using distributional methods. We experiment with several possible com- binations of representation and composition, exhibiting …...

. In Word Sense Disambiguation, there are still few usages of neural networks. This direction is very promising however, the results obtained by these first systems being systemat- ically in …...

AbstractMuch progress has been made in text summarization, fueled by neural architectures using large-scale training corpora. However, in the news domain, neural models easily overfit by leveraging position-related features due …...

AbstractThis paper presents Adam Mickiewicz University’s (AMU) submissions to the constrained track of the WMT 2022 General MT Task. We participated in the Ukrainian ↔ Czech translation directions. The systems …...