Корпус Википедии

Named Entity (NE) information is criti- cal for Information Extraction (IE) tasks. However, the cost of manually annotating sufficient data for training purposes, espe- cially for multiple languages, is prohibitive, …...

AbstractIn this paper, we present work on enhancing the basic data resource of a context-aware system. Electronic text offers a wealth of information about geospatial data and can be used …...

The geographical properties of words have re- cently begun to be exploited for geolocating documents based solely on their text, often in the context of social media and online content. …...

We propose a flexible and effective frame- work for extracting a bilingual dictionary from comparable corpora. Our approach is based on a novel combination of topic modeling and word alignment …...

AbstractIn this paper, we propose a new pipeline of word embedding for unsegmented languages, called segmentation-free word embedding, which does not require word segmentation as a preprocessing step. Unlike space-delimited …...

AbstractWe present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach …...

AbstractAbstract Meaning Representation (AMR) is a sentence-level meaning representation based on predicate argument structure. One of the challenges we find in AMR parsing is to capture the structure of complex …...