Using Robustness to Learn to Order Semantic Properties in Referring Expression Generation
| Autor: | Martín Ariel Domínguez, Pablo Ariel Duboué |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Referring expression generation Computer science 05 social sciences Natural language generation 02 engineering and technology Semantic property computer.software_genre 050105 experimental psychology Knowledge base Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer Natural language processing |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319479545 IBERAMIA |
| DOI: | 10.1007/978-3-319-47955-2_14 |
| Popis: | A sub-task of Natural Language Generation (NLG) is the generation of referring expressions (REG). REG algorithms aim to select attributes that unambiguously identify an entity with respect to a set of distractors. Previous work has defined a methodology to evaluate REG algorithms using real life examples with naturally occurring alterations in the properties of referring entities. It has been found that REG algorithms have key parameters tuned to exhibit a large degree of robustness. Using this insight, we present here experiments for learning the order of semantic properties used by a high performing REG algorithm. Presenting experiments on two types of entities (people and organizations) and using different versions of DBpedia (a freely available knowledge base containing information extracted from Wikipedia pages) we found that robustness of the tuned algorithm and its parameters do coincide but more work is needed to learn these parameters from data in a generalizable fashion. |
| Databáze: | OpenAIRE |
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