Multiword expressions (MWEs) consist of groups of tokens, which should be treated as a single syntactic or semantic unit. In this work, we focus on verbal MWEs (VMWEs), whose accurate recognition is challenging because they could be discontinuous (e.g., take .. off). Since previous English VMWE annotations are relatively small-scale in terms of VMWE occurrences and types, we conduct large-scale annotations of VMWEs on the Wall Street Journal portion of English Ontonotes by a combination of automatic annotations and crowdsourcing. Concretely, we first construct a VMWE dictionary based on the English-language Wiktionary. After that, we collect possible VMWE occurrences in Ontonotes and filter candidates with the help of gold dependency trees, then we formalize VMWE annotations as a multiword sense disambiguation problem to exploit crowdsourcing. As a result, we annotate 7,833 VMWE instances belonging to various categories, such as phrasal verbs, light verb constructions, and semi-fixed VMWEs. We hope this large-scale VMWE-annotated resource helps to develop models for MWE recognition and dependency parsing that are aware of English MWEs. Our resource is publicly available.
@InProceedings{KATO18.394, author = {Akihiko Kato and Hiroyuki Shindo and Yuji Matsumoto}, title = "{Construction of Large-scale English Verbal Multiword Expression Annotated Corpus}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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