Name
Abstract | ||
|---|---|---|
We present in this paper an experiment in automatically tagging a set of Portuguese modal verbs with modal information. Modality is the expression of the speaker's (or the subject's) attitude towards the content of the sentences and may be marked with lexical clues such as verbs, adverbs, adjectives, but also by mood and tense. Here we focus exclusively on 9 verbal clues that are frequent in Portuguese and that may have more than one modal meaning. We use as our gold data set a corpus of 160.000 tokens manually annotated, according to a modality annotation scheme for Portuguese. We apply a machine learning approach to predict the modal meaning of a verb in context. This modality tagger takes into consideration all the features available from the parsed data (pos, syntactic and semantic). The results show that the tagger improved the baseline for all verbs, and reached macro-average F-measures between 35 and 81% depending on the modal verb and on the modal value. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-09761-9_7 | COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE |
Field | DocType | Volume |
Verb,Parse tree,Computer science,Portuguese,Modal verb,Computational linguistics,Artificial intelligence,Natural language processing,Parsing,Linguistics,Syntax,Modal | Conference | 8775 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulo Quaresma | 1 | 18 | 2.73 |
| Amália Mendes | 2 | 1 | 0.35 |
| Iris Hendrickx | 3 | 1 | 0.35 |
| Teresa Gonçalves | 4 | 14 | 1.24 |