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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)

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Evaluating Document Coherence Modelling., , , , , and . CoRR, (2021)UniMelb_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity., , , , , and . *SEM@NAACL-HLT, page 207-215. Association for Computational Linguistics, (2013)Using Distributional Similarity of Multi-way Translations to Predict Multiword Expression Compositionality., , and . EACL, page 472-481. The Association for Computer Linguistics, (2014)UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures., , , , , and . SemEval@NAACL-HLT, page 851-856. The Association for Computer Linguistics, (2016)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)Differences in language use: Insights from job and talent search., , and . ADCS, page 3:1-3:4. ACM, (2019)Predicting the Compositionality of Multiword Expressions Using Translations in Multiple Languages., and . *SEM@NAACL-HLT, page 266-275. Association for Computational Linguistics, (2013)A Joint Model for Multimodal Document Quality Assessment., , , and . JCDL, page 107-110. IEEE, (2019)Feature-guided Neural Model Training for Supervised Document Representation Learning., , , and . ALTA, page 47-51. Australasian Language Technology Association, (2019)