Euskararako koma-zuzentzaile automatiko baterantz

XUXEN ortografia-zuzentzailearen arrakastaren ondoren eta IXA taldean Hizkuntzaren Prozesamenduan urtetan egindako lanari jarraiki, XUXENg euskarako gramatika- eta estilo-zuzentzailea garatzeko aurrerapausoak egiten dihardugu azken urteetan; horien artean kokatzen dugu hemen aurkeztuko dugun koma-zuzentzaile automatikoa ere. Tresna honen garapenerako, komak zuzen jartzeko lan teorikoak aztertu ditugu lehendabizi, eta ikasketa automatikoko teknikak eta erregeletan oinarritutakoak uztartu ditugu gero; koma-zuzentzaile bat garatzeko sintagmeneta perpausen identifikatzaile automatikoen beharra azaleratu du ikerketa honek

Extraction of Entailed Semantic Relations Through Syntax-Based Comma Resolution

This paper studies textual inference by investigating comma structures, which are highly frequent elements whose major role in the extraction of semantic relations has not been hitherto recognized. We introduce the problem of comma resolution, defined as understanding the role of commas and extracting the relations they imply. We show the importance of the problem using examples from Textual Entailment tasks, and present A Sentence Transformation Rule Learner (ASTRL), a machine learning algorithm that uses a syntactic analysis of the sentence to learn sentence transformation rules that can then be used to extract relations. We have manually annotated a corpus identifying comma structures and relations they entail and experimented with both gold standard parses and parses created by a leading statistical parser, obtaining F-scores of 80.2 % and 70.4 % respectively.

Extraction of Entailed Semantic Relations Through Syntax-based Comma Resolution

This paper studies textual inference by investigating comma structures, which are highly frequent elements whose major role in the extraction of semantic relations has not been hitherto recognized. We introduce the problem of comma resolution, defined as understanding the role of commas and extracting the relations they imply. We show the importance of the problem using examples from Textual Entailment tasks, and present A Sentence Transformation Rule Learner (ASTRL), a machine learning algorithm that uses a syntactic analysis of the sentence to learn sentence transformation rules that can then be used to extract relations. We have manually annotated a corpus identifying comma structures and relations they entail and experimented with both gold standard parses and parses created by a leading statistical parser, obtaining F-scores of 80.2 % and 70.4 % respectively.