A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language
João Sequeira | Teresa Gonçalves | Paulo Quaresma | Amália Mendes | Iris Hendrickx
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Modality annotation for Portuguese: from manual annotation to automatic labeling
Amália Mendes | Iris Hendrickx | Liciana Ávila | Paulo Quaresma | Teresa Gonҫalves | João Sequeira
Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning
We investigate modality in Portuguese and we combine a linguistic perspective with an application-oriented perspective on modality. We design an annotation scheme reflecting theoretical linguistic concepts and apply this schema to a small corpus sample to show how the scheme deals with real world language usage. We present two schemas for Portuguese, one for spoken Brazilian Portuguese and one for written European Portuguese. Furthermore, we use the annotated data not only to study the linguistic phenomena of modality, but also to train a practical text mining tool to detect modality in text automatically. The modality tagger uses a machine learning classifier trained on automatically extracted features from a syntactic parser. As we only have a small annotated sample available, the tagger was evaluated on 11 modal verbs that are frequent in our corpus and that denote more than one modal meaning. Finally, we discuss several valuable insights into the complexity of the semantic concept of modality that derive from the process of manual annotation of the corpus and from the analysis of the results of the automatic labeling: ambiguity and the semantic and syntactic properties typically associated to one modal meaning in context, and also the interaction of modality with negation and focus. The knowledge gained from the manual annotation task leads us to propose a new unified scheme for modality that applies to the two Portuguese varieties and covers both written and spoken data.
- Amália Mendes 2
- Iris Hendrickx 2
- Paulo Quaresma 2
- Liciana Ávila 1
- Teresa Gonҫalves 1
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