Annotating the MASC Corpus with BabelNet

Andrea Moro, Roberto Navigli, Francesco Maria Tucci, Rebecca J. Passonneau


Abstract
In this paper we tackle the problem of automatically annotating, with both word senses and named entities, the MASC 3.0 corpus, a large English corpus covering a wide range of genres of written and spoken text. We use BabelNet 2.0, a multilingual semantic network which integrates both lexicographic and encyclopedic knowledge, as our sense/entity inventory together with its semantic structure, to perform the aforementioned annotation task. Word sense annotated corpora have been around for more than twenty years, helping the development of Word Sense Disambiguation algorithms by providing both training and testing grounds. More recently Entity Linking has followed the same path, with the creation of huge resources containing annotated named entities. However, to date, there has been no resource that contains both kinds of annotation. In this paper we present an automatic approach for performing this annotation, together with its output on the MASC corpus. We use this corpus because its goal of integrating different types of annotations goes exactly in our same direction. Our overall aim is to stimulate research on the joint exploitation and disambiguation of word senses and named entities. Finally, we estimate the quality of our annotations using both manually-tagged named entities and word senses, obtaining an accuracy of roughly 70% for both named entities and word sense annotations.
Anthology ID:
L14-1323
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4214–4219
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/375_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/375_Paper.pdf