Towards deep content extraction from specialized discourse: the case of verbal relations in patent claims

• Autores: Gabriela Ferraro
• Directores de la Tesis: Leo Wanner (dir. tes.)
• Lectura: En la Universitat Pompeu Fabra ( España ) en 2012
• Idioma: español
• Tribunal Calificador de la Tesis: Horacio Rodríguez Hontoria (presid.), Horacio Saggion (secret.), Juan Manuel Torres Moreno (voc.), Kim Gerdes (voc.), Maarten Janssen (voc.)
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Inteligencia artificial
• Enlaces
  • Tesis en acceso abierto en:  TDX  e-Repositori UPF 
• Resumen

  • This thesis addresses the problem of the development of Natural Language Processing techniques for the extraction and generalization of compositional and functional relations from specialized written texts and, in particular, from patent claims.

    One of the most demanding tasks tackled in the thesis is, according to the state of the art, the semantic generalization of linguistic denominations of relations between object components and processes described in the texts. These denominations are usually verbal expressions or nominalizations that are too concrete to be used as standard labels in knowledge representation forms - as, for example, "A leads to B'', and "C provokes D", where "leads to" and "provokes" both express, in abstract terms, a cause, such that in both cases "A CAUSE B" and "C CAUSE D" would be more appropriate. A semantic generalization of the relations allows us to achieve a higher degree of abstraction of the relationships between objects and processes described in the claims and reduces their number to a limited set that is oriented towards relations as commonly used in the generic field of knowledge representation.