ArTS Archivio della ricerca di Trieste
ArTS è il sistema di gestione integrata dei dati della ricerca adottato dall' Università degli Studi di Trieste
EnglishThe problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets.
| Titolo: | Learning Text Patterns using Separate-and-Conquer Genetic Programming |
| Autori: | |
| Data di pubblicazione: | 2015 |
| Abstract: | The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets. |
| Handle: | http://hdl.handle.net/11368/2832545 |
| ISBN: | 9783319165004 9783319165011 |
| URL: | http://link.springer.com/chapter/10.1007/978-3-319-16501-1_2 http://link.springer.com/book/10.1007/978-3-319-16501-1 |
| Appare nelle tipologie: | 4.1 Contributo in Atti Convegno (Proceeding) |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 2015_EuroGP_LearningMultiPatterns.pdf | pdf post-print | Bozza finale post-referaggio (post-print) | Digital Rights Management non definito | Open Access Visualizza/Apri |
http://hdl.handle.net/11368/2832545
