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Determining the Multiword Expression Inventory of a Surprise Language.

, , and . COLING, page 471-481. ACL, (2016)

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Predicting the Compositionality of Multiword Expressions Using Translations in Multiple Languages., and . *SEM@NAACL-HLT, page 266-275. Association for Computational Linguistics, (2013)Umelb: Cross-lingual Textual Entailment with Word Alignment and String Similarity Features., , and . SemEval@NAACL-HLT, page 133-137. The Association for Computer Linguistics, (2013)Modelling Uncertainty in Collaborative Document Quality Assessment., , , , and . W-NUT@EMNLP, page 191-201. Association for Computational Linguistics, (2019)Feature-guided Neural Model Training for Supervised Document Representation Learning., , , and . ALTA, page 47-51. Australasian Language Technology Association, (2019)A Joint Model for Multimodal Document Quality Assessment., , , and . JCDL, page 107-110. IEEE, (2019)Automatic Identification of Persian Light Verb Constructions., , and . CICLing (1), volume 7181 of Lecture Notes in Computer Science, page 201-210. Springer, (2012)Automatic Identification of Expressions of Locations in Tweet Messages using Conditional Random Fields., , , , , and . ALTA, page 171-176. ACL, (2014)A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions., , and . ALTA, page 71-76. (2018)Detecting Non-compositional MWE Components using Wiktionary., , and . EMNLP, page 1792-1797. ACL, (2014)Evaluating hypotheses in geolocation on a very large sample of Twitter., and . NUT@EMNLP, page 62-67. Association for Computational Linguistics, (2017)