Name
Abstract | ||
|---|---|---|
Appositives are structures composed by semantically related noun phrases. In Natural Language Processing, the identification of appositives contributes to the building of semantic lexicons, noun phrase coreference resolution and information extraction from texts. In this paper, we present an appositive identifier for the Portuguese language. We describe experimental results obtained by applying two machine learning techniques: Transformation-based learning (TBL) and Hidden Markov Models (HMM). The results obtained with these two techniques are compared with that of a full syntactic parser, PALAVRAS. The TBL-based system outperformed the other methods. This suggests that a machine learning approach can be beneficial for appositive identification, and also that TBL performs well for this language task. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11874850_35 | IBERAMIA-SBIA |
Keywords | Field | DocType |
information extraction,noun phrase,hidden markov model,natural language processing,machine learning | Computer science,Natural language processing,Artificial intelligence,Syntax,Noun phrase,Coreference,Speech recognition,Lexicon,Natural language,Information extraction,Parsing,Semantics,Machine learning | Conference |
Volume | ISSN | ISBN |
4140 | 0302-9743 | 3-540-45462-4 |
Citations | PageRank | References |
6 | 0.52 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Claudia de Freitas | 1 | 6 | 0.52 |
| Julio C. Duarte | 2 | 26 | 2.46 |
| Cícero Nogueira dos Santos | 3 | 771 | 37.83 |
| Ruy Luiz Milidiú | 4 | 192 | 20.18 |
| Raúl P. Renterı́a | 5 | 47 | 5.77 |
| Violeta Quental | 6 | 6 | 1.53 |