Rule Induction for Sentence Reduction

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

Cordeiro, J (Autor) Outra; Dias, G (Autor) Outra; Pavel Brazdil (Autor) FEP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2013

Páginas: 528-539

16th Portuguese Conference on Artificial Intelligence (EPIA)

Angra do Heroismo, PORTUGAL, SEP 09-12, 2013

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-008-EG8

DOI: 10.1007/978-3-642-40669-0_45

Abstract (EN): Sentence Reduction has recently received a great attention from the research community of Automatic Text Summarization. Sentence Reduction consists in the elimination of sentence components such as words, part-of-speech tags sequences or chunks without highly deteriorating the information contained in the sentence and its grammatical correctness. In this paper, we present an unsupervised scalable methodology for learning sentence reduction rules. Paraphrases are first discovered within a collection of automatically crawled Web News Stories and then textually aligned in order to extract interchangeable text fragment candidates, in particular reduction cases. As only positive examples exist, Inductive Logic Programming (ILP) provides an interesting learning paradigm for the extraction of sentence reduction rules. As a consequence, reduction cases are transformed into first order logic clauses to supply a massive set of suitable learning instances and an ILP learning environment is defined within the context of the Aleph framework. Experiments evidence good results in terms of irrelevancy elimination, syntactical correctness and reduction rate in a real-world environment as opposed to other methodologies proposed so far.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12